A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

doi:10.4236/jbcpr.2013.14013

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Journal of Building Construction and Planning Research, 2013, 1, 89-130

Published Online December 2013 (http://www.scirp.org/journal/jbcpr)

http://dx.doi.org/10.4236/jbcpr.2013.14013

Open Access JBCPR

A Multi-Criteria Decision Support System for the Selection

of Low-Cost Green Building Materials and Components

Junli Yang, Ibuchim Cyril B. Ogunkah*

Department of Construction, School of Architecture and the Built Environment, University of Westminster, London, UK.

*Corresponding author: cyrilguchi@ymail.com

Received September 18th, 2013; revised October 25th, 2013; accepted November 4th, 2013

Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is

a guardian.

ABSTRACT

The necessity of having an effective computer-aided decision support system in the housing construction industry is

rapidly growing alongside the demand for green buildings and green building products. Identifying and defining finan-

cially viable low-cost green building materials and components, just like selecting them, is a crucial exercise in subjec-

tivity. With so many variables to consider, the task of evaluating such products can be complex and discouraging.

Moreover, the existing mode for selecting and managing, often very large information associated with their impacts

constrains decision-makers to perform a trade-off analysis that does not necessarily guarantee the most environmentally

preferable material. This paper introduces the development of a multi-criteria decision support system (DSS) aimed at

improving the understanding of the principles of best practices associated with the impacts of low-cost green building

materials and components. The DSS presented in this paper is to provide designers with useful and explicit information

that will aid informed decision-making in their choice of materials for low-cost green residential housing projects. The

prototype MSDSS is developed using macro-in-excel, which is a fairly recent database management technique used for

integrating data from multiple, often very large databases and other information sources. This model consists of a data-

base to store different types of low-cost green materials with their corresponding attributes and performance character-

istics. The DSS design is illustrated with particular emphasis on the development of the material selection data schema,

and application of the Analytical Hierarchy Process (AHP) concept to a material selection problem. Details of the

MSDSS model are also discussed including workflow of the data evaluation process. The prototype model has been

developed with inputs elicited from domain experts and extensive literature review, and refined with feedback obtained

from selected expert builder and developer companies. This paper further demonstrates the application of the prototype

MSDSS for selecting the most appropriate low-cost green building material from among a list of several available op-

tions, and finally concludes the study with the associated potential benefits of the model to research and practice.

Keywords: Analytical Hierarchy Process (AHP); Decision Support System (DSS); Low-Cost Green Building Materials;

Decision Analysis; Material Selection Factors

1. Introduction

As the green building movement begins to sweep

through the housing construction industry, the applica-

tion of cost effective and energy efficient building mate-

rials has become necessary in today’s demanding eco-

nomic market [1,2]. Recent discussions on the need to

lower the growing demand for conventional sources of

energy have highlighted the value of using low-cost

green building materials and components, given their

lower cost and energy requirements [3,4]. Evidence from

previous studies has proven that implementing such

products in construction has the potential to not only re-

duce health and environmental effects, but to also bring

savings from energy, maintenance, and operational costs

[5-9]. Yet, research has consistently shown that the pa-

tronage for such materials in housing construction is still

at a very low level in comparison to many other conven-

tional building materials [8,9]. Recent studies [10,11]

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

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argue that several attempts to adopt low-cost green

building materials for housing design projects have gen-

erally been viewed as challenging, given that most de-

signers are vaguely informed about the full life-cycle

impacts of such products. They note that information

relating to the impacts of such building materials in the

housing construction sector appears to be less available,

as evidence [11,12] indicates that only a small proportion

of design and building professionals seem to have suffi-

cient knowledge that could allow effective decision-

making. Ashraf [11] and Zhou et al. [12,13] suggest that

maximizing their potential use in the housing industry

requires seamless access to appropriate informed infor-

mation and full understanding of the various options

available, so as to inform decision trade-offs at the de-

sign stage.

Despite the availability of accurate and reliable data,

Seyfang [14] and Malanca [15] however, noted that most

designers are found to make decisions regarding the se-

lection of such materials on the basis of their past ex-

perience. They observed that inexperienced designers

generally engage the traditional mode of selection, by

relying on subjective individual perceptions of values

and priorities in the material selection process, which

rather than facilitate or drive their design ideas, appear to

do the opposite thereby limiting creativity and sometimes

resulting in considerable frustration [16,17].

Trusty [18-21] & Woolley [22] further disclosed that

existing databases on such materials and their formats are

not designed to efficiently and directly provide such in-

formation to decision makers. They note that the avail-

able data on such materials are normally in the read-only

format, and are stored in various operational databases

that are not easily accessible to decision makers in usable

forms and formats. As a result, decision-making failures

during the planning and design stage(s) of low-cost green

housing projects hinder their use in terms of their indus-

trial capacity utilisation in the housing industry.

While several studies [14,15,20] have emphasized the

relative importance of information access in aiding

well-considered and justifiable material choices during

the early stages of the design process, Wastiels et al. [16]

argue that the existing material selection method focuses

mainly on limited aspects of such materials, in terms of

their properties and factors that influence the decision-

making process. Quinones [17] asserted that some low-

cost green building materials, for example, contain high

embodied energy that leads to ecological toxicity and

fossil fuel depletion impacts during their manufacturing

phase. She argued that ignoring the relevant factors or

properties of any of such materials during the crucial

material selection phase could reduce the effective life of

that product to less than half of its normal effective life

span.

Moreover, Seyfang [14] and Trusty [18-22] argue that

choosing the right materials for a particular project can

be a very complex decision-making task, given that the

selection process is influenced and determined by nu-

merous preconditions, decisions and considerations.

They suggested the idea of a decision support system

(DSS) as a useful aid in making quick and critical deci-

sions during crucial material selection process. They

stressed that the considered approach to encourage the

wider scale use of low-cost green building materials in

mainstream housing should enable design professionals

to have easy access to adequate information on the

available options, hence, making the selection results

more reasonable and bringing more standardization to the

material selection decision-making process at the design

stage. They went on advising that whatever method is

employed must be such that it allows comparison of not

only the cost or technical performance of such materials,

but also able to take into account several decision-mak-

ing criteria, so as to derive conclusive and valid evidence

of the differing impacts of various material alternatives.

While there seem to be no compelling evidence of

technical research on a holistic approach used by design

professionals for the evaluation and selection of building

materials, previous material assessment models such as

the Leadership in Energy and Environmental Design

(LEED) and Building Research Establishment Environ-

mental Assessment Methods (BREEAM), have shown

great promise for guiding evaluations of material predic-

tor performance [23]. The findings of the main research

study yet, criticised and noted the flawed existing support

systems for being partially objective and fraught with

problems of fairness [24]. The study revealed that exist-

ing methods are found wanting in that they are culturally

implicit, and that such methods or tools treat the sustain-

ability [of the] wider built environment as simply a mat-

ter of energy and mass flows with little or no regard to

the socio-economic, technical, emotive and political di-

mensions of sustainability [24]. It further revealed that

individual country teams establish scoring weights sub-

jectively when evaluating building products, which often

pose problems when applied to other regions [25,26].

The analysis of the study however, showed little evi-

dence to justify the assumption that there are tools of

demonstrable reliability for designers to assess the sus-

tainability and suitability of such materials or products or

their applicability and utility for their potential use in the

design of low-cost green housing projects. Hence, a more

reliable method is needed to aid design and building pro-

fessionals in the selection of such building materials and

components for low-cost green residential housing pro-

jects.

Consequently, to promote more informed decision-

making in the selection of low-cost green materials both

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

Open Access JBCPR

individually and as assembled building components, a

Decision Support System (DSS) is presented in this pa-

per as an aid to design and building professionals. The

objective of this study is to support decision-makers in

selecting low-cost green building products that are envi-

ronmentally, socio-culturally, technically and economi-

cally balanced through a proposed conceptual system.

The model is to facilitate the integration of more sus-

tainable materials into future designs by helping design-

ers quantify how they compare to materials already per-

mitted under existing codes, using the concept of the

Analytical Hierarchy Process (AHP). The AHP approach

is designed to be practical, as it combines environmental,

technical, socio-cultural and economic performance into

a single performance value that is easily interpreted.

In the following sections, the reviews of existing tech-

nological approaches are summarised and the main find-

ings and themes to emerge from the literature review and

the fieldwork seminars and interviews are reported. Then

a step-by-step methodology is presented to illustrate the

different stages of the DSS model development. Finally,

the application of the prototype DSS for selecting appro-

priate floor material for a residential project in the Lon-

don Borough of Sutton is demonstrated. The final section

concludes the study and suggests areas for further re-

search.

2. Technology in Material Selection: Review

For the past ten years, proven and commercialized tech-

nologies have been developed to promote environmental

awareness amongst built environment professionals

[18,22,23]. Empirical research validates that various

studies on building material selection support systems

have developed in size and specification within the last

ten years [24-26]. Castro-Lacouture et al. [26] note that

the application of green building support/assessment

tools has been widely accepted as an effective and useful

way of promoting green housing construction in the

housing construction industry. Keysar and Pearce [27]

and Bayer et al. [28] however, argue that the contexts in

which building environmental assessment methods now

operate, and the roles that they are increasingly playing,

are qualitatively different than earlier expectations. They

note that material assessment tools are now classified

based on the type of analysis they perform, such as

product, assembly, or whole building analysis, or classi-

fied as region-specific tools, either considered based on

the life-cycle phases they cover, or on the required skills

necessary to operate the tool.

While there is clearly an urgent need for new tech-

nologies to optimise the use of low-cost green building

materials, it is also true that there are many technologies

or systems already in use [25-28]. The first real attempt

to establish a comprehensive means of simultaneously

assessing a broad range of environmental considerations

in building materials was the Building Research Estab-

lishment Environmental Assessment Method (BREEAM)

[28]. The BREEAM tool assesses the environmental im-

pacts of over 150 various materials and components most

commonly used in home construction. The tool takes

environmental issues into account, then adds measure-

ments and user-defined weighting to arrive at environ-

mental impacts, measured as “Eco-points” for each

building material being assessed. Twelve different envi-

ronmental impacts are individually scored, together with

an overall summary rating, which enables users to select

materials and components according to overall environ-

mental performance over the life of the building. This

scientifically accepted program however, focuses only on

the environmental performance of products rather than

environmental, social and financial considerations going

hand in hand as parts of the material evaluation and se-

lection process.

With emphasis on the Leadership in Energy and Envi-

ronmental Design tool (LEED), Keysar and Pearce [27]

conducted a detailed evaluative study comparing the ef-

fectiveness of five different relative importance indices

for selecting appropriate material selection tools such as:

relative advantage; compatibility; complexity; trialability;

and observability, with the goal of improving the sus-

tainability of materials for capital projects. Here, materi-

als such as; regionally manufactured materials, materials

with recycled content, rapidly renewable materials, sal-

vaged materials, and sustainably forested wood products

are selected based on credit scores. Analyses of their

study however, revealed that the LEED model for exam-

ple specifically requires an energy model, a task often

handled by a specialist within a design firm or out-

sourced to a third party specializing in energy modeling.

Due to the inflexibility inherent in the application of

first generation tools, and since they tend to require

greater technical expertise to implement, many different

tools of the second generation group have also been

launched to address these limitations. Among this cate-

gory is the ATHENA estimator. This has been one of the

most popularly used material data-analytic models that

analyses over 1200 building material and assembly com-

binations [28]. It allows the users to look at the life cycle

environmental effects of a complete structure or of indi-

vidual assemblies and to experiment with alternative de-

signs and different material mixes to arrive at the best

scenario. Bayer et al. [28] noted that the major draw-

backs to this tool are the fixed assembly dimensions,

software cost, the cost and required skills to use it, the

limited options of designing high-performance assem-

blies, and the overall incomplete assessment of whole

buildings environmental impacts [28-30].

With the identified setback associated with ATHENA

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

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estimator, The National Institute Standards and Tech-

nology (NIST) developed the Building for Environ-

mental and Economic Sustainability (BEES®) 4.0. This

model provides a cradle-to-grave product-to-product

comparison of over 230 building products based on

manufacturer and supply company information [28-30].

The impact categories are weighed, normalized, and

merged into a final environmental performance score, to

generate a single measure of desirability for product al-

ternatives by combining qualitative and quantitative data.

The BEES 4.0 model is however, not capable of provid-

ing data for a full LCA of a complete building product,

as it only produces data for a limited amount of building

materials and evaluative factors [28-31]. These single-

attribute claims ignore the possibility that other life-cycle

stages or environmental impacts can yield offsetting im-

pacts. Other limitations include; limited product options,

limited use for local/regional impact materials and de-

valuating weighing process [17].

Trusty [18-21] argued that these sets of first and sec-

ond-generation tools less often consider any of the Multi-

Criteria Decision Methods available to solve MCDM

problems, adding that some systems do not even consider

Life Cycle Cost (LCC) and other performance criteria

simultaneously or completely. Moreover, he claimed that

the existing performance requirements/criteria approach

used in such tools tend to rely on immeasurable charac-

teristics in demonstrating the extent of sustainability in a

product, which makes them over-burdensome to imple-

ment and communicate.

Since the highlighted material assessment tools were

developed primarily to be used in different countries, and

the data sources used by each tool differed, further ef-

forts have been undertaken to develop knowledge-based

or expert DSS for assistance in material selection. For

instance, Rahman et al. [32,33] developed an integrated

knowledge-based cost model for optimizing the selection

of materials and technology for residential housing de-

sign using Technique of ranking Preferences by Similar-

ity to the Ideal Solution (TOPSIS). The system is devel-

oped to assist architects, design teams, quantity surveyors

and self house builders to make decisions for the design

from early stage to detailed design stage by ranking the

performance and cost criteria of technologies and materi-

als. Loh et al. [34] however, criticised the tool for pro-

viding partial assistance in the material selection process

of the whole building design as it only considers the cost

of roofing materials. They argue that material selection

process depends on a number of other factors such as the

location, zoning and environmental regulations, demo-

graphic characteristics, etc. that are not considered in

their system. They note that the TOPSIS approach

adopted does not only lack the ability to eliminate bias in

the selection process but also unable to allow fairer

trade-off process.

Loh et al. [34] emphasise that strategic selection of

sustainable materials and building design prior to the

building construction is crucial to increasing building life

cycle energy performance. They argue that stakeholders

involved in the early design process often have conflict-

ing priorities for both building design and construction

materials. They developed an environmentally focused

decision support system in the form of an Environmental

Assessment Trade-off Tool (EATT), which supports the

development of the ideal building design and materials

combination that meets stakeholders’ requirements. It is

designed to assist users select the most appropriate mate-

rial among a set of candidate materials based on the ana-

lytical hierarchy process (AHP) concept of decision-

making, since AHP technique has the robust ability to

handle the complexities of real world problems, and to

deal formally with judgment error, which is distinctive of

the AHP method. The system rank orders a set of prese-

lected, technically feasible materials using different deci-

sion factors with and without tangible values, such as a

clients favour over a particular building design, publicity

potential of the building design, life cycle cost, capital

cost and energy performance of different materials and

building layouts. Zhou et al. [12] argued that the ap-

proach adopted by Loh et al. [34] lacked in robustness as

it does not take into account the full-life cycle impacts of

newly-accepted building products, and did not specify

the sort of materials under studied.

Zhou et al. [12] developed a decision support multi-

objective optimization model for sustainable material

selection. The material selection tools and material data

sheets provide extensive information that includes factors

such as cost, mechanical properties, process performance

and environmental impact throughout the life cycle based

on expert knowledge. Wastiels et al. [16], confirmed that

the tool, however, lack the considerations or descriptions

to evaluate the intangible aspects of building materials,

which are important to architects. They also criticised the

selection methodology for being highly restrictive to a

limited range of factors and incompatible with other

stakeholders.

Ashby and Johnson (2002) introduce “aesthetic attrib-

utes” in the material properties list for product designers

when describing material aspects such as the transpar-

ency, warmth, or softness. Within the discipline of archi-

tecture, however, the intangible qualities of materials are

not described and mapped within the current design

models. No selection framework was provided to support

the implementation of a system.

Wastiels et al. [16], proposes a qualitative and quanti-

tative framework to support informed decisions based on

physical aspects’ and “sensorial aspects” of building ma-

terials, but without the tools integration and computerisa-

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

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tion as done by Zhou et al. [12]. In the presented frame-

work, no pronouncement is made upon how sustainable

considerations from these different categories could in-

fluence each other, and what MCDM approach could

possibly be used if developed.

A similar study by Ding [35], developed a comprehen-

sive assessment decision support system that measures

the environmental characteristics of a building product

using a common and verifiable set of criteria and targets

for building owners and designers to achieve higher en-

vironmental standards. Upon analysis it was found that

the assessment for her study focused heavily on envi-

ronmental issues rather than the broader social, cultural,

technical and economic aspects of sustainable green con-

struction.

Keysar & Pearce [27] cited extensive research litera-

ture describing how material selection tools facilitate the

innovation diffusion process and radical decision-making

transformation. They however, note that most of the ex-

amined models make choices that result in “fabricated

assemblies of standardized performance attributes”, im-

plying that they do not choose for materials but rather for

‘material systems’.

Hopfe et al. [36] conducted a study that assessed the

features and capabilities of six software tools to screen

the limits and opportunities for using BPS tools during

early design phases. The tools classification was based

on six criteria namely the capabilities, geometric model-

ing, defaulting, calculation process, limitation and opti-

mization. However, the authors did not report what

methodology was used to compile these criteria.

A cost modeling system for roofing material selection

was further proposed in Perera and Fernando [37]. Sev-

eral factors were identified and considered in the selec-

tion process. Results demonstrated large inconsistency in

the evaluation process. No particular reference was made

to the selection methodology.

Other influencing reviews within the scope of this

study include Mohamed and Celik [38] who proposed a

computerised framework that is responsible for materials

selection and cost estimating for residential buildings

where users are able to choose their preferred one from

list of materials without evaluation and synthesis of mul-

tiple design criteria and client requirements. No mention

was made about the MCDM technique used for evaluat-

ing the list of materials selected and their respective

quantities.

Mahmoud et al. [39] suggested a method for the selec-

tion of finishing materials that covered floors, walls and

ceilings and integrates cost analysis at the appropriate

decision points, but without the selection information

requirements or methodology as proposed in this study.

Lam et al. [40] carried out a survey on the usage of

performance-based building simulation tools. His study

examined the relative impacts and limitations of knowl-

edge-base tools in decision-making. Murray argues that

while there is a natural tendency for design and building

professionals to focus on the scientific and technological

aspects of green and sustainable construction, their ap-

proach does not necessarily maximise the positive con-

tributions professionals have to offer if tools are designed

to replace professional judgment in the choice of materi-

als. Murray suggests that this is because tools cannot

address the intrinsic motivations people need if they are

to embrace the positive changes sustainability requires.

He continues that limiting the assembly of buildings to

the specification of systems would impede the discovery

of design opportunities inherent in materials themselves.

Similar patterns of consistency, and lack thereof, have

also been obtained [for detailed reviews see 17,24-27,31].

By highlighting the different green building material

assessment tools, it can be deduced that existing tools are

dispersed and based on individual initiatives without a

unified consensus based framework [41,42]. It is appar-

ent that each tool has its own unique application. While

each tool could be called an LCA tool, there was little

consistency in the methodologies used from one tool to

another. In addition, while one tool considered the build-

ing as a system, other tools considered primarily the

product’s individual attributes rather than how that spe-

cific product performed within the building system [42].

A key question therefore, is whether current assessment

methods that were conceived and created to specifically

evaluate the environmental merits of conventional build-

ing materials can be easily transformed to account for a

qualitatively different set of materials.

Giorgetti & Lovell [43] for instance have reported the

sub-optimal performance of existing tools. They argued

that the subjective values and priorities of the authors of

the assessment scheme largely dictate the technical char-

acteristics of the systems, and currently represent the

major focus of discussion. They suggest that it is neces-

sary for potential users to analyse the local situation and

identify the adaptability of using any tool before apply-

ing a universal green building assessment tool to a spe-

cific country and region. They warned that some existing

tools such as BREEAM, LEED, and even current expert

tools might potentially institutionalize a limited defini-

tion of environmentally responsible building practice at a

time when exploration and innovation should be encour-

aged in another region.

However, in all the reviewed studies, no efforts to de-

velop a DSS that associates with the corresponding at-

tributes and performance characteristics of low-cost

green building materials and components, starting from

the broad list of available options in the database to the

final selection of the most appropriate material, were

found in the existing literature [43,44].

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The findings of the review have shown that each of the

indices applied in developed regions to deal with issues

associated with the impacts and performance of low-cost

green building materials in other regions have proven

unsatisfactory [44,45]. This finding is premised on the

fact that most existing material selection systems have

been designed by countries with more developed econo-

mies such as the UK, where the scale of social issues and

lack of access to resources is simply not as critical as

observed in the developing nations [45,46]. The setbacks

that associates with the tools reviewed in this research

thus, highlights the opportunity for developing a Material

Selection Decision Support System (MSDSS), to better

address the specific needs and attributes specific to the

use of low-cost green materials for tool adopters new to

green housing.

The following section briefly highlights the aim and

objectives of the study. It extensively describes specific

methods adopted for each task in Section 3.1.

3. Research Methodology

In order to identify the key selection factors or variables

that formed the basis for the development of the proto-

type multi-criteria decision support system (DSS), suit-

able clusters of research approaches were considered in

the research exercise, some of which include: exploratory

literature reviews, networking with domain experts and

practitioners, series of questionnaire surveys and knowl-

edge-mining interviews [47]. Table 1 provides an over-

view of the research aim, objectives and the methodology

undertaken in four major stages.

3.1. Research Design

To provide a clear theoretical framework for the rela-

tively new area of study, and develop preliminary ideas

on issues specific to the research theme within the con-

text of decision-making associated with the impacts of

low-cost green building materials and components in

housing construction, this study reviewed relevant litera-

ture through synthesis and analysis of recently published

data, using a range of information collection tools such

as; books, and peer-reviewed journals from libraries and

internet-based sources. Recognising the limitations of the

literature review in terms of examining current research

thinking in respect of decision support systems for the

selection of low cost green building materials and com-

ponents, a preliminary research study was undertaken to

check and validate prior assumptions in the background

and review sections.

In order to build upon knowledge gained from the lit-

erature review, and recognising the limitations of the

preliminary research survey in terms of examining cur-

rent research thinking in respect of decision support sys-

tems for low cost green building materials and compo-

nents, a mixed method was adopted for this study. This

was followed by in-person interviews to further clarify

and elaborate on less detailed and pertinent issues asso-

ciated with the use low-cost green building materials.

The in-depth interviews consisted of 10 participants, who

involved a sample of practicing architects, engineers,

material specifiers, and a host of building profession-

als-who influence material choice decisions in the UK

housing construction industry. This approach was used to

examine the potentials of the proposed MSDSS, (being a

tool for the assessment and evaluation of low-cost green

materials). It further investigated the effectiveness of

design and decision support tools, as well as identified

requirements of Life Cycle Assessment (LCA) tools for

design decisions at the various stages of the design proc-

ess.

Consequently, a quantitative questionnaire was devel-

oped as the result of the analysis of the results from the

interviews. In order to elicit the “most important” factors,

a questionnaire survey was conducted among the execu-

tives of some selected builder/developer firms. They

were asked to rank order from a list of factors (compiled

from existing literature on the topic and after initial con-

sultation with some of the executives) based on their

judgment and experience. The executives were also

asked to indicate desired features they would like to have

in a DSS for low-cost green material selection. Since the

respondents were widely dispersed, and because it was

anticipated that building professionals would be more

likely to reply and cooperate with a less time-consuming

research method, giving the constraints of time, wider

coverage, and budget, it was therefore, decided that a

questionnaire sent and returned by email would be the

most convenient way of collecting the required data. The

inclusion of qualitative open-ended questions provided

respondents a chance to express their views more freely.

The target groups of respondents were also taken from

a database or directory of building professionals provided

by the UK, China, Canada, South Africa, Brazil and US

Green Building Councils (GBCs). The selection ap-

proach followed the random sampling technique to avoid

bias and uneven sample sizes amongst different profes-

sional groups, and ensure uniformity, consistency and

quality of data. To facilitate the response rate, snowball

sampling was also adopted, where the approached re-

spondents were asked to distribute the questionnaire to

their colleagues and partners within the field [47].

The selection of South Africa and Brazil for the analy-

sis was due largely to their great similarities in social,

economic, and geopolitical terms, and likewise their de-

veloped counterparts. In a similar vein, the choice of

building experts within the selected countries was as a

result of their expertise and advancement in the use and

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

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Table 1. Basic summary of the research methods.

AIM

To develop a decision support system (DSS) that will provide designers with useful and explicit information associated

with low-cost green building materials and components, to aid informed decision-making in their choice of materials for

low-cost green residential housing projects.

Stage Objectives Tasks Method

1.Examine current views on themes

related to decision-making associated with

the use of low cost green materials in the

housing industry, to identify new ideas &

issues arising from the study

Step 1. Reviewed relevant literature through synthesis and

analysis of recently published data, using a range of

information collection tools such as; books, peer-reviewed

journals, and articles from libraries and internet base sources

AA,

1: REVIEW 2. Review various DSSs currently used at

national and international levels for the

selection of materials to identify

knowledge deficits and the potential

benefits associated with their use

Step 2. Carried out a preliminary research study with leading

researchers who influence the selection of building materials

in the field of housing construction

AA, QS,

INT

Step 3. Conducted a pilot study, by deploying a

test-questionnaire to a small sample of researchers who

possess relevant knowledge on issues specific to the use of

low cost green materials using the email addresses taken from

the databases of recognised building construction companies

and research institutions

Step 4. Conducted the main survey, by administering the

revised questionnaire through email contacts taken from

databases of interested registered building professional groups,

who influence the selection of construction materials from

throughout the construction value chain

Step 5. Conducted in-person interviews with interested

building professionals who influence material choice decision

in housing construction using audio recording system to avoid

re-contacting the respondents or falsification of information

2: DATA

COLLECTION

&SYNTHESIS

3. Conduct surveys and interviews with

building professionals, to identify the

potential factors or variables that influence

the informed selection of low cost green

building materials and components

Step 6. Carried out inspection on available expert systems

most commonly used in building firms in the UK, USA, China

etc. by interviewing experts, with years of experience in the

industry, who have implemented or used such systems and

directly observing how they function when in operation

AA, QS,

INT

3: DATA ANALYSIS

4. Evaluate and establish the weighted

importance of the key factors or variables

that will help to determine the relative

impacts of the different choices of

building materials and components

Step 7. Analysed the information and report gathered from the

survey exercise(s) using a suite of statistical analytical

programs, and various quantitative data analytical techniques

AA, QS

Step 8. Assembled the key components by synthesising the

relevant databases to be incorporated in developing the

proposed DSS model.

5. Develop a system to integrate the

necessary information appropriate to the

informed selection of low-cost green

building materials & components Step 9. Developed the main structure workflow of the

proposed system by creating links among the various

databases,

AA, QS,

Step 10. Inputted relevant data to test the internal links to

know what needed to be measured within the system, and

checking the output of the results against easily calculated

values

Step 11. Conducted experts survey by deploying a sample of

the prototype system via email of those who participated in the

main survey, using feedback questionnaires as a quicker and

cost effective means of assessing respondents’ judgments

about the system

Step 12. Made necessary changes based on the feedback from

the survey M

4: DEVELOPMENT

6. Test the functionality of the proposed

approach; and validate the effectiveness

by applying it to a building material

selection problems using a series of case

study residential building projects in the

Step 13. Validate the modified prototype system using a series

of completed building projects in the UK, by comparing the

outputs from the algorithms to monitored data from the

completed building

M, CS

KEYS: AA (Archival analysis); INT (Interview); CS (Case study); QS (Questionnaire Survey); M (Modeling).

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

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development of green building tools (as they have had

the most uptakes in both geographical regions and being

part of an emerging market).

To receive a reasonably sized sample, 500 surveys

were sent out by email, over a two-month period of

March and April 2012. Using a progressive approach of

data collection, a total of 250 respondents returned the

completed survey, representing a response rate of 50%.

The response rate was accepted as the normal ranges

between 20% - 30% were found in most of the construc-

tion industry related research [33,34]. Prior to distribu-

tion, the questionnaire was pre-tested for comprehensi-

bility by consulting five academics at two universities

[47]. A number of changes were suggested and imple-

mented.

Respondents were also invited to post their ideas about

current limitations or improvements that should be

avoided or integrated in the development of the proposed

MSDSS model at the later part of the questionnaire. The

questionnaire also examined the adequacy/inadequacy

between traditional manual approach of material selec-

tion and computer-aided decision support tools. One of

the group’s participants commented that one of the hall-

marks of good science is that a result can be tested inde-

pendently and proven to be right or wrong in the latter

method. The analysis of the questionnaire survey and

interviews provided a list of “key” decision-related fac-

tors having significant impacts on the process of material

selection for residential development as shown in Section

4.1.1.

3.2. Research Findings

The results of the study however, revealed the following.

• Many existing decision support systems in the devel-

oped countries do not have the appropriate perform-

ance threshold for addressing the most relevant issues

specific to less developed countries;

• Current DSS models are unable to relate to matters

associated with the informed selection of materials

that are commonly used for housing projects in coun-

tries with rather less-mature markets;

• The lack of informed knowledge by building profes-

sionals in terms of the principles, characteristics, and

best practices relevant to the use of low-cost green

materials at the design stage, has been identified as a

common constraint peculiar to their wider-scale use

in the housing industry;

• The majority of building professionals still regard

cost and environmental factors as conventional pro-

ject priorities when selecting building materials or

components, but rarely consider the implications of

social, political, technical, sensorial, legal and cultural

factors in their choice of materials; and finally,

• The majority of low-cost green building materials are

yet to be certified under the building regulations,

standard specifications and codes of practice; and

most importantly,

• There are no demonstrable and compelling evidence

of technical research on a holistic approach used by

design professionals for the evaluation and selection

of low cost green building materials and components

at the design stage.

The results of the study thus, provided the platform

that suggested the need for a system that could aid in-

formed decision-making to improve understanding, and

enhance the effectiveness of actions to implement and

promote the wider-scale use of low-cost green building

materials and components at the core of the construction

business process. In light of their feedback and useful

suggestions from building experts who partook in the

study, the following portions of the DSS model were

either readjusted or improved.

• Easy searchable material selection inputs database;

• Ability to add/remove material selection features with

ease;

• Ability to make custom reports;

• Ability to easily navigate all components with ease;

• Comprehensive “HELP or USER INSTRUCTIONS”

menu explaining what the tool is doing;

• Being able to understand the material selection proc-

ess through the lens of non experts;

• Ability to perform trade-off analysis to compare dif-

ferent material options;

• Clarity on the algorithms used to perform the simula-

tions; and Real-time results;

• Data input forms to ensure easy and consistent data

input; and,

• Having a huge amount of customizability in terms of

output.

After the improvement, the system was shown to the

same participants, and minor adjustments were made on

the basis of second feedback. In the following sections

the proposed MSDSS selection methodology is discussed,

and a conceptual framework for the decision support

system based on the methodology is presented. Subse-

quently, the MSDSS model is applied to a hypothetical

but realistic material selection problem to rank order the

candidate materials for selecting the most appropriate

one.

4. System Development

For this research, AHP was selected for its simplicity and

due to the fact that it can be easily implemented using

any spreadsheet software application such as the MS

Excel, as it possesses a powerful macro language that is

essential since a menu driven interface had to be devel-

oped. Since the intention of the research was not to de-

velop a commercial software product, Macro-in-Excel

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VBA (MEVBA) was utilized for the following reasons:

• Macro-in-Excel VBA (MEVBA) has the capabilities

to perform all necessary calculations and is common

enough that most people are familiar with it;

• It has the ability to write scripts that could automati-

cally convert material data from any graphic table

format to an appropriate condensed data table (hidden

from the user’s view) to allow quick and reliable in-

dexing of material data;

• The Macro-in-Excel VBA framework has the code

that makes Windows forms work, so any language

can use the built-in code in order to create and use

standard Windows forms;

• Makes the application easier to maintain; With

MEVBA, codes were easily built into the form or re-

port’s definition, since the DSS model contained a

large number of macros that respond to events on

forms and reports; which would have been difficult to

maintain using any other application;

• With Macro-in-Excel VBA it was easy to step

through a set of records one record at a time and per-

form an operation on each record;

• Macro-in-Excel VBA helped to supply a standard

security mechanism, which was made available to all

parts of the MSDSS data application model;

• Enables the developer to create his own functions:

The MSDSS contains a series of mathematical model

and computational algorithmic procedures that pro-

vided a basis for computing the green development

index of material alternatives within an integrated de-

cision-support framework or tool(s).

• Ability to mask error messages during the tests run;

• Enables the system to quickly analyze existing data to

discover trends so that predictions and forecasts can

be made with reasonable accuracy;

• Allows for extensions and expansions: since the

components of the framework are modular, meaning

that each may be developed independently, and data

may be added as it is acquired to supplement the

knowledge and databases, macro-in-excel was used to

achieve that goal

4.1. MSDSS Database/Data Warehouse Design

The data warehouse design constitutes the major portion

of the MSDSS development and hence will be explained

in detail in this section. The data warehouse design es-

sentially consists of four steps as follows:

Step 1: Identifying the key influential factors that will

impact on the choice of materials;

Step 2: Designing the material selection methodology

framework and identifying the objectives of each step;

Step 3: Designing the various components of the

MSDSS model and defining their features and functions;

Step 4: Defining the workflow selection methodology

and analytical procedure of the actual prototype MSDSS

model

4.2. Identifying the Key Influential Factors

In order to identify the relative importance of the sub-

categorical factors or variables based on the survey data,

ranking analysis was performed. Five important levels

were transformed from Relative Index values: Highly

Significant Level (H) (0.8 ≤ RI ≤ 1), High-Medium Level

(H–M) (0.6 ≤ RI < 0.8), Medium Level (M) (0.4 ≤ RI<

0.6), Medium-Low Level (M–L) (0.2 ≤ RI < 0.4), and

Low Level (L) (0 ≤ RI < 0.2).

From the results of the analysis, 40 factors were iden-

tified under the “Highly significant” level for evaluating

low-cost green building materials with an RI value rang-

ing from 0.952 to 0.806 and a total of 15 factors, were

recorded to have “High-Medium” importance levels with

an RI value ranging from 0.795 to 0.652. The analysis of

the main survey identified a total of 55 key influential

factors out of 60 initial factors as important components

of the material selection process.

“Life Expectancy” was ranked as the first priority in

the technical category with an RI value of 0.952, and it

was also the highest among all factors and was high-

lighted at “High” importance level. “Resistance to fire”

was also rated high in importance among the selection

factors. “Maintenance Cost” was ranked third in impor-

tance. It was clear from this research that there is a per-

ception of ambiguity surrounding the long-term mainte-

nance of low-cost green building materials. This is not

entirely any surprise given that maintenance free build-

ings are increasingly sought after by clients, anxious to

minimise the running costs associated with buildings.

“Life-cycle cost” has been, and will continue to be, major

concerns for building designers, as well as important

traditional performance measure.

Among the top 20 ranking factors, it was observed that

only one factor from the environmental category out of

the list was ranked high among the selection factors. This

again suggests that environmental issues within the con-

text of the developing countries are not strongly consid-

ered despite the high environmental awareness exhibited

by design and building professionals in developed re-

gions. This finding also corroborates the initial observa-

tions of various studies [14,15] repeatedly highlighted in

the background and literature studies. They suggest that

the problems within the developing regions are charac-

terised by mainly social and economic issues, unlike the

developed regions where the scale of social issues and

lack of access to basic resources are simply not much of

a problem as it is in the developing world.

From Figure 1, a total of 15 factors, consisting of 12

site factors, 1 socio-cultural factor, and 2 sensorial fac-

tors, were recorded to have “High-Medium” importance

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levels. Although these 15 variables were in the same im-

portance level category, the “building orientation” factor

within the “general/site category” (average RI = 0.652)

was considered to be the least important variable com-

pared to the factor “Glossiness” under the “sensorial

category” (with an average RI = 0.774), and “material

availability” still under the “general/site category” (with

an average RI = 0.795). However, it should be noted that

site factor accounted for 75% in the “High-Medium”

importance level. The result is an example of evidence

pointing to the trend that environmental and perhaps site

issues are no longer considered as the most important

factors for material selection in housing projects, espe-

cially within the context of the less developed regions.

Some factors in the three categories were ranked rela-

tively higher in the “High-Medium” level. For example,

“material availability (GS1)” was rated as first in the

general/site subcategory, and ranked as thirty-fifth in the

overall ranking with an RI value of 0.795. An interesting

observation from the results is that none of the criteria

fell under the medium and other lower importance level.

This clearly shows how important the factors are to

building designers in evaluating low-cost green building

materials. All factors were rated with “High” or “High-

Medium” importance levels. However factors such as

Compatibility with other materials, Skills availability,

and UV resistance fell within the medium-low level. The

findings of the analysis asserted that the criteria with

medium or low RI does not mean they are not important

for selecting materials, but rather created an opportunity

to highlight the relative importance of the key criteria

from their vantage points. The following shows a frame-

work consisting of the key factors in their order of im-

portance.

4.3. Designing the MSDSS Selection

Methodology

The diagram shown in Figure 2 demonstrates the con-

ceptual framework of the selection methodology for the

decision support system. Table 2 describes a step-by-

step procedure of the selection methodology for the ma-

terial selection decision support system. Section 4.4 pre-

sents various components of the MSDSS schema or

model.

4.4. Designing the Features of the MSDSS Model

The next stage of the model development was to design

the various features of the databases containing the logic

and showing relationships between the data organized in

different modules. Each module contains the physical

information and contents needed to aid in the material

evaluation and selection process.

!!!

) G

l/Site

cto

GS2-Material Availability

GS1-Geographic Location of Site

GS10-Building and Space Usage

GS9-Knowledge Base in Construction

GS6- Withstand Natural Disasters

GS7-The Type of Building Material(s)

GS4-Building Certification for Use

GS5-Design Co ncept

GS12-Spatial Scale: Building Size and Mass

GS8-Project Site G eometr y/Setting/Condition

GS3-Distance

GS11-B uil d ing O r ien tati on

)

EH3-Safety and Health of End-users

EH6-The Climatic Condition of the Region

EH7-Material Environmental Impact

EH2-Level of Carbon Emissions and Toxicity

EH4-Habitat Disruption: Ozone Depletion Potential

EH1-Environmental Statutory Compliance

EH5-The Amount of Pesticide Treatment Required

)

mic/C

C4-Maintenance or Replacem en t Cos t

C5-Labour or Installation Cost

C1-Total Life Cycle Cost

C3-Capital/Initial Cost

C2-Material Embod ied Energy Cost

(SC ) Soci

Cul

l Fac

SC5-Knowledge of the Custom

SC1-M a te ria l Co m p atibility with T ra di t ion s

SC6- C ompatibility with Clien t’s Preference

SC2-Material Compa tibility with Regional Settings

SC3-Cultural Restriction(s) on Usury

SC4-Family Structure: Type & Size of Family Unit

(SN) Sensorial Factor

SN4-Temperature

SN6-Odour

SN10-Lighting Effect

SN5-Acoustics

SN1-Aesthetics or Visual density

SN2-Texture

SN3-Colour

SN7-Thickness/Thinness

SN9-Hardness

SN8-Glossiness/Fineness

SN11-Structure

SN12-Translucence

(T) T

chnic

T15-Life Expectancy

T7-Resistance to Fire

T9-Resistance to Moisture

T11-Resistance to Wea ther

T5-Availability of the Technical Skills

T8-Resistance to Heat

T13-Resistance to Decay

T3-Level of Maintenance Requirement

T6-Ease and Speed of Met hod fixing

T4- Expansion-Contraction Tolerance

T1-Recyclability and Reusabilit y

T12-Resistance to Chemicals

T2-Ease to Remove/Re-Affix/Replace

T14-Weight & Mass of material

T10- Resi stance to Scratch

T-16-Renewability

T17- Compatibility with other Materials

T18-UV Resistance

Material AlternativesPref er r ed Mater ial

Choice

Analytical Hierarchy Process

Figure 1. Ranked factors for measuring the impacts of low-cost green building materials.

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Table 2. Description of the selection methodoly.

OBJECTIVE TASK

1. Define or state overall objective/goal The first step of the methodology is to define the main goal of the intended task.

2. Identify Set of all Possible Material Alternatives to be

Assessed

After defining the main goal of the task, the next step is to generate the set of all

possible alternatives that are available for selection based on the decision-making

parameters. In the material selection process, this comprehensive set of alternatives

includes all construction materials and components currently in the database, and the

market in context.

3. Prune all infeasible alternatives from set

The third step is to reduce the complete set of alternatives by eliminating/pruning those

alternatives, which are clearly infeasible for the intended application from the database

consisting of all materials, based on classifications of materials according to the

Construction Standards Institute (CSI) Divisions, and material heuristics. For example,

if the element under consideration is a structural beam, materials such as roofing sheet

and glass are automatically pruned from the set of possible alternatives under

consideration, since none of these materials fall under the CSI structural divisions. This

should result in a subset of alternatives, all of which would be feasible choices for the

intended application. The “pruning” approach is used rather than allowing the user to

select feasible materials from the whole set because users tend to overlook alternatives

which might be unfamiliar to them but are nonetheless feasible.

4. Evaluate Remaining Alternatives

• Weight Attributes (Decision Factors)

• Calculate Values for Attributes

• Amalgamate Weighted Attributes

• Develop Ranking

The fourth step in the methodology is to evaluate the feasible alternatives using the

AHP model such that a ranking can be developed according to the relative importance

of the material for the intended application.

• First, the decision maker weights each factor or variable according to the relative

importance that the decision factor or variable holds for the decision maker. It

involves the decision-maker replacing probabilities with user weightings for each

factor or variable to supplement, not replace, his judgment.

• Second, values for each of the factors or variables are determined for each material

with regard to the manufacturer’s information & details of the material or

component contained in the material database, and then, a normalized value

between zero and one is calculated for each factor value.

• After weights have been established and values calculated for each attribute

against a set of materials or components, the weights and normalized values are

multiplied and summed to create an index of preference for that alternative(s).

• Then, a list of alternatives ranked according to the relative importance of the

factors or variables is then presented.

5. Review Ranking of Alternatives

When the indices of factors or variables have been calculated for all feasible

alternatives, a ranking is developed sorting the alternatives according to each utility

value based on the AHP model of decision-making. The alternative with the highest

utility value is recommended from the ranked list of potential materials for each

design/building element.

6. Select Alternative Based on Ranking

The decision maker may then either elect/decide to select the highest ranked

alternative, or choose another alternative from the set based on his professional

judgment.

7. Proceed to Next Design Elements The decision maker satisfied with the selection process, then proceeds to the next

design/building element.

The conceptual model/framework of the prototype

MSDSS tool consists of a number of interconnected

modules/features. A logical model illustrating the devel-

oped DSS for material selection is shown in Figure 3.

Table 3 describes the functions of each component of the

MSDSS model.

4.5. How the System Works

The following steps explain how the prototype MSDSS

model works during the material evaluation process.

Step 1: The load manager provides the user with a list

of design elements from the “Design Elements” module,

and then prompts the user to select the design element of

his/her choice in accordance with the terms and specifi-

cations of the Construction Standards Institute (CSI) Di-

visions;

Step 2: The User then selects the particular design

element needed for the intended task from a list of design

elements (as broken down by the Construction Standard

Institute Division);

Step 3: User then enters values for the relevant pa-

rameters to answer prompts about areas and dimensions

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Figure 2. Selection methodology for the MSDSS model.

Figure 3. Conceptual framework of the MSDSS model.

of the selected design element, and then sets the thresh-

old values in the material knowledge base

Step 4: The system validates the design parameters

and threshold details entered by the user, and then gener-

ates the set of all feasible material alternatives that are

available for selection, (which includes all categories of

construction materials contained in the materials data-

base);

Step 5: After a set of feasible material alternatives has

been generated for the “particular design element”, the

system through the “Weighting Score Extractor Module”

prompts the user to obtain weightings for the desired

parent and sub-factors according to the relative impor-

tance that each factor or variable holds over another

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101

based on the decision maker’s preference of value;

Step 6: After weights have been established and values

calculated for each factor for a particular material, the

weights and normalized values are multiplied and sum-

med to create an index of subjective utility for each al-

ternative;

Step 7: The alternative with the highest utility value is

recommended by the system;

Step 8: The user reviews the system’s recommended

choice for each element in the “Result” module, and then

either selects the highest ranked alternative, or chooses

another alternative from the set based on professional

judgment and/or the system’s recommendation.

Step 9: The user may choose to generate a printout re-

port or graphical representation of the list of selected

materials and green utility indices if desired.

Step 10: The selection process then proceeds to the

next design element.

Figure 4 presents a graphical representation of the

system workflow.

An illustrative example of the AHP concept is dis-

played and explained in following section to demonstrate

the selection process by applying the prototype MSDSS

model to a hypothetical case study design project.

5. Application

The following example illustrates the selection process of

floor covering products. It selects the best one among

three alternatives. The prototype MSDSS, developed

using the AHP technique, was used to select the most

appropriate residential building floor material for hous-

ing development in the city of London, located in the

Sutton County of London. The results demonstrate the

capabilities of the MSDSS system in a real-life but hy-

pothetical application scenario. In the following section

this process of application is described and discussed.

5.1. A Hypothetical Study Case

The next stage of the model development was to design

the various features of the databases containing the logic

and showing relationships between the data organized in

different modules. Each module contains the physical

information and contents needed to aid in the material

evaluation and selection process. Table 4 summarizes

the details for the three options of flooring materials for

the proposed residential low-cost green housing project.

The description of the three options in Table 4 was based

on the standard practices and construction details com-

monly used in the housing construction industry.

These three (3) floor materials described above will be

analysed amongst a host of other material alternatives for

the selection of a more sustainable option. In other words,

this section will analyse the problem using the MSDSS

model, which relies on the use of the AHP mathematical

multi-criteria decision-making technique, to identify and

decide which material is the most sustainable and suit-

able flooring material in this case.

To achieve this goal, the MSDSS model was sent to 10

Table 3. Functions of the features of the MSDSS model.

MSDSS Features Functions

1. Design Elements and Parameters This feature provides users with a range of building design elements and their respective

parameters

2. Material Rule Base

This feature articulates the listing of individual materials in prescribed sequences, gradually

eliminating candidate materials based on their inability to meet stated material selection

heuristics/rules.

3. Material Choice Generator This feature contains the material/component database, which generates the set of all possible

material alternatives that are available for selection.

4. User’s Weightings Sets preferred weighting value for all attributes to compare with.

5. Weighting Extractor This feature queries the user to obtain weightings for the factors, based on the user’s preference of

value on a scale of 1 - 9.

6. Material Index Evaluator The material index evaluator calculates values of the selected factors or variables for each feasible

material choice.

7. Amalgamator

Here the user’s weightings are amalgamated (i.e. multiplied and summed) with the factor values or

weightings for each potential material, resulting in a relative ranking of the feasible materials for

each element.

8. Results

- This component provides the ability to view the processed data, and to generate reports. It allows

the MSDSS model User Interface to communicate with the user; and also connects all the reports

and queries that are generated in the Monitoring databases to the corresponding project files.

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Figure 4. Workflow of the MSDSS model.

Table 4. Summary of the flooring options.

Description Material A Material B Material C

Design Element Type Paneled Flooring Laminated Flooring Concrete Flooring

Building Type Residential Residential Residential

Material Type Bamboo XL laminated Split

Paneled Flooring

Reclaimed/Recycled Laminated

Wood Flooring and Paneling

Fly Ash Cement concrete

Floor Slab

Size of Materials 230 mm × 150 mm 50 mm × 6000 mm 900 mm × 900 mm

expert evaluators who had the following qualities:

• Considerable amount of knowledge in material analy-

sis based on the AHP concept;

• Used a wide range of green building assessment tools

for material selection; and

• Taken part in the previous survey.

The aim of this exercise was to compare their view of

the prototype MSDSS model with existing models in

terms of their usability, flexibility, and interoperability

attributes using the concept of the Analytical Hierarchy

Process (AHP).

5.2. Rationale for Adopting the AHP Concept

The study adopted the use of the AHP technique to in-

vestigate the interrelationships amongst various criteria

and low-cost green material alternatives due to the fol-

lowing reasons:

• AHP is a method that is conceptually easy to use, and

decisionally robust to handle the complexities of real

world problems;

• It does not require the very strong assumption that the

stakeholders make absolutely no errors in providing

preference information;

• It has the ability to deal formally with judgment error,

which is distinctive of the AHP method;

• The AHP method provides the objective mathematics

to process the unavoidably subjective preference in-

herent in real- world evaluations;

• Possesses an inherent capability to handle qualitative

and quantitative criteria important for sustainable

material selection; and finally,

• Can enable all members of the evaluation team to

visualize the problem systematically in terms of par-

ent criteria and sub-criteria.

Figure 5 shows the flowchart of the material selection

computational analysis technique based on the concept of

the Analytical Hierarchy Process model. The following

sections present details of the evaluation exercise.

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Figure 5. Flowchart of the AHP concept.

5.3. Applying the AHP Model to the Problem

According to Reza et al. [48], AHP is a subjective

MCDM method that does not necessarily involve or rely

on a large sample for its analysis. To better illustrate the

procedure of the AHP technique of decision-making,

with reference to the case presented in Section 5.1, a

complete example of applying AHP to the problem of

material selection is provided here based on evaluators’

results. Twenty (20) respondents representing various

fields of the housing construction industry, and who had

fore knowledge of the AHP procedure were selected to

participate in the AHP survey.

By evaluating the consistency level of the collected

questionnaires, 5 questionnaires out of the 10 received

had acceptable consistency and were entered into the

system. In order, to avoid arbitrary and inconsistent an-

swers in the data, the mean values of five (5) out of the

ten (10) respondents were used to fill out the pair-wise

comparison matrices for the parent and sub-factors.

The package included the model, evaluation question-

naire and a cover letter stating the purpose of the re-

search, the validation process and what was expected of

them. To conduct the exercise, the study adopted Chua’s

et al. [49] approach based on a number of suggestions as

follows:

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• A document that reminded and explained the overall

aim and objectives of the study to the respondents,

followed by a step-by-step demonstration of its op-

eration;

• A demo illustrating a practical exercise. This allowed

the evaluators the experience of using the system en-

sued. During the practical assessment session of the

demo, evaluators were able to see the controls and get

a general overview of the MSDSS interface;

• An illustrative example of the objective and method-

ology of the AHP technique based on the instructions

in the demo, to guide and illustrate to every respon-

dent on how to browse and conduct analysis;

• After the introduction, a feedback questionnaire was

forwarded to the evaluators;

• After each evaluation, each evaluator highlighted

their experience(s) and provided feedback on the feel

of the system, with special attention to the problems

that they encountered during the evaluation process;

• Finally, a reflective or post-user questionnaire was

completed to obtain feedback;

• Evaluators were asked to answer each statement or

question relating to the model in the questionnaire

based on their personal view(s);

• They were also asked to assess the importance of the

system based on their perception. Evaluators were

also asked to add general comments on the system,

and provide feedback on the applicability of the pro-

totype system in assisting in specific material selec-

tion problems during their experience and other ways

of improvement;

• Problems uncovered or areas that proved difficult to

understand during the evaluation process were imme-

diately modified so that it did not arise in subsequent

sessions, as this procedure followed each evaluation;

• The respondents were instructed of the relevance of

observing consistency in their answers whilst using

the MSDSS model;

• The questions relating to different aspects were pre-

sented in different sections. This helped respondents

to focus on one aspect at a time.

The following sections exemplify the process.

5.4. Decomposition of the Decision Problem

The evaluation exercise provided users with the opportu-

nity to define the problem. Figure 6 shows the exem-

plary hierarchy of the problem. The goal is placed at the

top of the hierarchy. The hierarchy descends from the

more general or parent factors in the second level to sub-

factors in the third level to the alternatives at the bottom

or fourth level as shown in Figure 6). To select a suitable

choice among alternatives, the users were instructed to

define the decision factors needed for the analysis. In

other words, the users determined which alternative

could be the best choice to meet the goal considering all

the selected decision factors or criteria displayed in Fig-

ure 6.

The first step of the methodology (as illustrated in fig-

ure 2) was to define the main goal of the intended task,

by identifying the design element needed for the analysis,

and inputting the relevant dimensional scale for the sug-

gested design element (see Figure 7(a)).

After defining the main goal of the task, the next step

was to generate the set of all possible alternatives that

were available for selection with reference to the deci-

sion-making parameters as shown in Figure 7(b). At this

stage the users are prompted or alerted by the MSDSS

model to identify a set of feasible floor material alterna-

tives based on a range of material selection heuristics/

knowledge-based rules. The goal is to choose a suitable

floor material among options for the project case de-

scribed in Section 5.1.

5.5. Pair-Wise Comparison of Parent Factors

After selecting the design element, and identifying a set

of feasible alternatives using the material selection heu-

ristics/knowledge-based rules, the respondents were

made to perform pair-wise comparisons following the

demo instruction guide of the MSDSS model. This in-

cluded the analysis of all the combinations of parent fac-

tors and sub-factors relationships. The sub-factors were

compared according to their relative importance (based

on the ratio scale proposed by Saaty [50-55], with respect

to the parent element in the adjacent upper level. After

performing all pair-wise comparisons by the decision-

makers, the individual judgments were aggregated, bas-

ing its analysis on the geometric mean technique as Saaty

suggested [52-55].

5.6. Pair-Wise Analysis of the Parent Factors

To avoid arbitrary and inconsistent answers in the data

obtained from the 10 participants who consented to par-

taking in the study, the mean values of five (5) out of the

ten (10) respondents were used to fill out the pair-wise

comparison matrices for both the parent and sub-factors.

The pair-wise comparison matrices obtained from 5 re-

spondents were combined using the geometric mean ap-

proach at each hierarchy level to obtain the correspond-

ing consensus pair-wise comparison matrices [54-56].

Using the verbal/ratio scale shown in Figure 8, respon-

dents obtained weightings for each parent factor, based

on the preference of value(s) on a scale of 1 - 9. The

MSDSS model then automatically translated each of the

matrixes into the corresponding largest eigenvalue prob-

lem and was solved to find the normalised and unique

priority weights for each factor (as shown in Figure 9).

Going by Saaty’s [55] rule, the judgment of a respondent

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SELECTING A PPR OPR IATE LOW-COS T GREEN BUILDING MATERI AL

GENERAL/SITE

FACTOR

ECONOMIC

FACTOR

ENVIRONMENTAL

FACTOR

SOCIO-CULTURAL

FACTOR

TECHNICAL

FACTOR

SENSORIAL

FACTOR

GS4-Building

Certification

GS1-Location

GS2-

Availability

GS3-Distance

GS5-Designers’

Experience

GS5-Disaster

Prone

GS6-Site

Geometry

GS8-Spatial

Structure

GS9- Spatial

Activities

GS10- Material

Scale

EH1- Env

Compliance

-CO

Emissions

EH3-Users’

Safety

EH4-Ozone

Depletion

EH5-Pesti cide

Treatment

EH6-Climate

EH7 -E nv -

Toxicity

EH8-Fossil

Depletion

EH9-Nu c l ear

Waste

EH10-

aste

Disposal

C1-Life -Cc

cle

Cost

C2-Embodied

Energy C ost

C3-Capital Cost

C4-LabourCost

Replac e ment

Cost

C6-Maint.enace

Cost

SC1-Com

atible

(Tradition)

-Com

atle

(Region)

SC3-Controlon

Usury

SC4-Clients’

Preference

SC5-Custom

Knowledge

T1-Recyclability

T2-Removability

T3-Maintenance

T4-S tr ess Tolerance

T5-Available Skills

T6-Fixing Speed

T7-Fire Resistance

SN1-Aesthetics

-Texture

SN3-Colour

SN4-

Temperature

SN5-Acoustics

SN6-Odour

SN7-

Thick/Thin

SN8-Glosiness

SN9-Hardness

SN10-Lighting

Effect

Goal

Main Fa

ors

Sub-Factors

erna

rnati

Choic

rnati

Choic

rna

Choic

T8-Thermal Resist

T9-Moisture Resist

T10 - Sc r at ch R e s i st

T11

her

T12-Chemica l Resist

T13-Resist De ca

T14-

eight

T15-Life Expectancy

T17-U V Resis t anc e

Comp

SN11-

Translucence

SN1

Structure

Figure 6. Hierararchy of the material selection phases.

is accepted if the Consistency Ratio (CR) ≤ 0.10. In cases

were the results of the respondents were not consistent,

the participants were alerted or prompted by the model to

carefully re-evaluate the factors until consistency was

achieved.

Figures 9 and 10 represent the principal matrix of

comparison, which contains the comparison between

main/parent factors in relation to the overall objective of

the problem (i.e., the selection of a sustainable low-cost

green building floor material). From Figure 9, it is pos-

sible to observe that factor SC is 3 times more important

than factor EH. As a logical consequence, factor EH is 3

times less important than factor SC. It is also possible to

observe that the elements in the principal diagonal are

always equal to 1. In other words, the weight of a crite-

rion in relation to itself, obviously, is always 1.

From Figure 9, it is also possible to observe that com-

paring Socio-cultural [SC] and Technical [T] factors, the

participants slightly favoured Technical aspects of the

products [T], thus arrived at an average value of two (2),

derived from the mean calculation of the five respon-

dents. Comparing Socio-cultural [SC] impacts with Sen-

sorial [SN], participants somewhat considered Socio-

cultural [SC] as more relevant in their choice of materials

than the emotive or sensorial [SN] aspects of the prod-

ucts, thus arriving at a mean score of 2. Comparing

Technical [T] and Sensorial [SN], Technical [T] issues

where proven to be more relevant or more slightly fa-

voured than others making it the most dominant factor of

the three. Based on their preference values, the system

automatically creates a reciprocal matrix on the opposite

end as the case may be.

At this stage (as shown in Figure 11), ratio scales are

defined for pair-wise comparison of the main or parent

factors using the ratio scale of 1 - 9. As mentioned earlier,

the decision makers obtained values for each parent fac-

tor based on their aprioristic knowledge and individual

weighting preference. Here, the AHP main criteria matrix

is then automatically developed by comparing the rela-

tive importance of one parent factor over the other as

shown above in Figure 11.

Next, the parent criteria matrices are normalised (by

dividing a cell value by the sum of each column) and

then checked for consistency using Eigen values as

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(a)

(b)

Figure 7. (a) Dimensional scale for the elected design element; (b) Selection rules for the elected design element.

Figure 8. Ratio scale for pair-wise comparison of factors.

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Figure 9. Consensus pair-wise comparison of main factors.

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Figure 10. Consensus pair-wise comparison of main factors.

Weighted Criteria Matrix

General/Site

Environment

Economic/Cost

Socio-Cultural

Technical

Sensorial

General/Site 1.00 0.33 0.17 0.11 0.13 0.11

Environment/Health 3.00 1.00 0.33 0.33 0.33 0.17

Economic/Cost 6.00 3.00 1.00 0.33 0.33 0.50

Socio-Cultural 9.00 3.00 3.00 1.00 0.50 2.00

Technical 8.00 3.00 3.00 2.00 1.00 3.00

Sensorial 9.00 6.00 2.00 0.50 0.33 1.00

Total 36.00 16.33 9.50 4.28 2.63 6.78

Figure 11. Results of pair-wise analysis of parent factors.

shown in Figure 12. A local priority vector score is then

generated for the matrix of judgments by normalizing the

vector in each column of the matrix (i.e. dividing each

entry of the column by the column total) and then aver-

aging over the rows of the resulting matrix [55]. The

normalized eigenvector shown in Figure 12 represents

the relative importance of each parent criteria.

Based on the calculation in Figure 11, the relative pri-

orities of the parent factors in the final selection of a sus-

tainable floor material were calculated as displayed in

Figure 12. The resulting local priority vectors were

given as: (GS = 0.030, EH = 0.070, C = 0.120, SC =

0.240, T = 0.340, and SN = 0.200) as shown in Table 5.

In order to measure the level of consistency of the ma-

trix for the parent factors, the consistency index (CI) was

then calculated at 0.103 (see Figure 11). The random

index (RI) was also taken into consideration and values

calculated at this stage of the evaluation exercise. Ac-

cording to Saaty (2008), for matrix of order 6, the RI is

1.24 (see Table 6). Given the two values (consisting of

both the consistency index (CI = 0.103) and the relative

index (RI= 1.24), the CR was then calculated as:

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Normalised Average Criteria Matrix

General/Site

Environment/Health

Economic/Cost

Socio-Cultural

Technical

Sensorial

Av. λMAX

General/Site 0.03 0.02 0.02 0.03 0.05 0.02 0.03 0.934297901

Environment/Health 0.08 0.06 0.04 0.08 0.13 0.02 0.07 1.113775203

Economic/Cost 0.17 0.18 0.11 0.08 0.13 0.07 0.12 1.162609985

Socio-Cultural 0.25 0.18 0.32 0.23 0.19 0.30 0.24 1.04719097

Technical 0.22 0.18 0.32 0.47 0.38 0.44 0.34 0.880596922

Sensorial 0.25 0.37 0.21 0.12 0.13 0.15 0.20 1.377336489

Total 1.00 1.00 1.00 1.00 1.00 1.00 1.00 6.52

Matrix Size 6

RI 1.24

CI 0.103

CR 0.083064516

Figure 12. Relative priority scores of the parent factors.

Table 5. Derived priority scores of the parent factors.

Factor/Criterion Relative Priority

General/Site 0.030

Environmental/Health 0.070

Economic/Cost 0.120

Socio Cultural 0.240

Technical 0.340

Sensorial 0.200

Table 6. Random index values for 1 ≤ n ≤ 15.

n 1 2 3 4 5 6 7 89 10 11 12 13 1415

RI 0 0 0.58 0.9 1.12 1.24 1.32 1.411.45 1.49 1.51 1.54 1.55 1.571.58

CR = CI/RI = 0.103/1.24 = 0.083 (see Figure 11).

According to the AHP model, a matrix is considered

as being consistent when the CR is less than 10%. With a

Consistency Ratio (CR) of 0.083, the matrix was consid-

ered consistent since it was less than 0.1.

5.7. Pair-Wise Analysis of Sub-Factors

The results of the next pair-wise comparison matrices

amongst the relative sub-factors are shown from Figures

13-24. The same calculations done for the principal ma-

trices of the parent factors were also done for the matri-

ces of the sub-factors. The local priority vector and the

consistency ratio for each sub-criterion matrix were also

computed and displayed on each corresponding table as

fully displayed below.

After comparing each sub-factor according to the

user’s system of value over other sub-factors, the weight-

ings were obtained to establish each priority weightings

in the context of the overall goal: selecting the most sus-

tainable low-cost green floor material. The criteria ma-

trices of each sub-factor were then normalised (by divid-

ing a cell value by the sum of each column) and then

checked for consistency as shown in Figures 13-24.

5.8. Determining the Weightings of Sub-Factors

The next stage of the assessment process was to find the

final weightings of both the parent and sub-factors that

will be used subsequently to evaluate the material attrib-

utes for sustainable building material selection. To de-

termine the final weightings of the selected factors, the

priority vectors (1) of the parent factors are multiplied by

the corresponding relative priority vectors of each

sub-criterion weighting vectors (2) to obtain the (final)

weighting (3) as shown in Table 7.

The main/parent factor weighting is derived from us-

ers’ judgment with respect to a single main criterion. The

resultant value of the comparison of each parent factor

serves as the priority vector of the main criteria needed

for evaluating material attributes. The selected value for

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Table 7. Derived final weightings for G-site factors.

Parent factor/Criteria

Weighting (1)

Sub-Factor/Criteria

Weighting (2)

Final

Weighting (3)

Criteria User

Value Default CR

<0.1

Selected

Value Sub-Criteria User ValueCR

< 0.1

Selected

Value Total = 1.0000

General/Site 0.03 0.057 0.08 0.026 GS1-Location (Mph) 0.197 0.09 0.197 0.0051

GS2-Material Availability 0.158 0.158 0.0041

GS3-Distance to Market

(km/h) 0.127 0.127 0.0033

GS4-Building Certification

code 0.115 0.115 0.0030

GS6-Withstand site natural

disaster 0.083 0.083 0.0022

GS8-Conforms to site

geometry 0.114 0.114 0.0030

GS9-Conforms to spatial

structure 0.069 0.069 0.0018

GS10-Conforms to all spatial

activities 0.053 0.053 0.0014

GS11-Conforms to design

geometry 0.044 0.044 0.0012

GS12-Mat. Spatial scale/Size

(sq./m) 0.040 0.040 0.0010

each parent factor as shown in Table 7 include: GS =

0.026, EH = 0.068, C = 0.122, SC = 0.245, T = 0.335 and

SN = 0.203

The sub-factor weighting is derived from user’s judg-

ment with respect to each sub-factor. Some of the se-

lected values that serve as the corresponding relative

priority vectors of the general/site variable include: 0.197,

0.158, 0.127, 0.115, 0.083, 0.114, 0.069, 0.053, 0.044,

and 0.040 as shown in Table 7.

Final weighting is derived from multiplying the se-

lected value of the main criteria-weighting or priority

vector by the selected value of the sub-factor priority

vector. This entry is obtained as follows: 0.026 × 0.197=

0.005122 (as highlighted in Table 7). The same process

was applied to the other parent factors of the respective

categories. The following steps describe the ways by

which the various weighting vectors of each criterion are

derived.

5.9. Pair-Wise Comparison of the Selected

Material Alternatives against Each

Sub-Factor

The final step of the exercise was for the respondents to

compare each pair of low-cost green material alternatives

with respect to each sub-factor. Here the user evaluates

the criteria/factors and material alternatives by compar-

ing them through direct rating, to know which factor is

more important; how many times; and which material

alternative is better in the context of each factor.

The corresponding weightings were based on the im-

portance that the evaluators attached to the dominance of

each material alternative relative to all other alternatives

under each sub-criterion. These matrices were also nor-

malized and checked for consistency as shown in Fig-

ures 25-38.

Figures 25-38 present some results of the analyses,

which explain the pair-wise matrix priority weightings

and normalisation of the various materials with respect to

each sub-criterion.

5.10. Determining the Weightings of Sub-Factors

The next phase, after analysing the pair-wise matrices of

the sub-factors against the various low-cost green floor

material alternatives was to normalize the priority

weights for each pair-wise comparison judgment matri-

ces. Once the normalised matrices of the floor material

alternatives and various sub-factors were obtained, the

values derived from the analysis were multiplied and

summed to obtain the final composite priority weights of

all material alternatives, focusing particularly on the

three floor materials used in the fourth level of the AHP

model of decision-making shown in Figure 6.

In this case, the final weighting scores (obtained from

multiplying the priorities vectors of the parent criteria

with that of individual sub-factors), is further multiplied

by the priority vector of each material alternative after

the pair-wise comparison against each sub-factor (as

shown in Figure 38). This resulted in a final composite

priority/weighting score of each sub-factor for the three

floor material alternatives.

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Score GS1 GS2 GS3 GS4 GS6GS8 GS9 GS10 GS11 GS12

GS1-Location (Mph) 0.197 1.002.003.002.004.002.00 2.00 2.00 3.00 3.00

GS2-Material Availability 0.158 0.501.002.002.002.003.00 3.00 3.00 2.00 3.00

GS3-Distance to Market (km/h) 0.127 0.330.501.002.002.002.00 3.00 3.00 3.00 2.00

GS4-Building Certification code 0.115 0.500.500.501.002.002.00 3.00 2.00 4.00 2.00

GS6-Withstand site natural disaster 0.083 0.250.500.500.501.002.00 2.00 2.00 2.00 2.00

GS8-Conforms to site geometry 0.114 0.500.330.500.500.501.00 3.00 7.00 3.00 4.00

GS9-Conforms to spatial structure 0.069 0.500.330.330.330.500.33 1.00 3.00 3.00 2.00

GS10-Conforms to all spatial activities 0.053 0.500.330.330.500.500.14 0.33 1.00 2.00 2.00

GS11-Conforms to design geometry 0.044 0.330.500.330.250.500.33 0.33 0.50 1.00 2.00

GS12-Mat. Spatial scale/Size (sq./m) 0.040 0.330.330.500.500.500.25 0.50 0.50 0.50 1.00

CR 0.09

Figure 13. Pair-wise matrix for general/site factors.

Normalised Matrix λMAX λMAX 11

0.210 0.315 0.333 0.208 0.296 0.153 0.110 0.083 0.127 0.130 0.935 Matrix

Size 10

0.105 0.157 0.222 0.208 0.148 0.229 0.165 0.125 0.085 0.130 0.999 CI 0.14

0.070 0.078 0.111 0.208 0.148 0.153 0.165 0.125 0.127 0.086 1.147 RI 1.49

0.105 0.078 0.055 0.104 0.148 0.153 0.165 0.083 0.170 0.086 1.103 CR 0.09

0.052 0.078 0.055 0.052 0.074 0.153 0.110 0.083 0.085 0.086 1.123

0.105 0.052 0.055 0.052 0.037 0.076 0.165 0.291 0.127 0.173 1.486

0.105 0.052 0.037 0.034 0.037 0.025 0.055 0.12 0.127 0.086 1.248

0.105 0.052 0.037 0.052 0.037 0.010 0.018 0.041 0.085 0.086 1.265

0.070 0.078 0.037 0.026 0.037 0.025 0.018 0.020 0.042 0.086 1.042

0.070 0.052 0.055 0.052 0.037 0.019 0.027 0.020 0.021 0.043 0.920

Figure 14. Normalised matrix for general/site factors.

Score EH1EH2EH3EH4EH5EH6EH7 EH8 EH9EH10

EH1-Env. Statutory Compliance 0.202 1.004.003.002.002.003.003.00 2.00 2.002.00

EH2-Embodied CO2 Emission (KgCO2/m2) 0.124 0.251.002.003.002.002.002.00 2.00 3.000.50

EH3-Human Toxicity-Users Safety level 0.113 0.330.501.002.002.002.003.00 3.00 3.000.50

EH4-Ozone depletion rate 0.086 0.500.330.501.002.002.002.00 2.00 2.000.33

EH5-Amt. of Pesticide Treatment (l/m2) 0.078 0.500.500.500.501.002.003.00 2.00 0.330.50

EH6-Complies with the Climate of the region 0.067 0.330.500.500.500.501.002.00 2.00 2.000.50

EH7-Env. Toxicity (land, water, Animals) 0.053 0.330.500.330.500.330.501.00 2.00 2.000.33

EH8-Fossil fuel/Habitat depletion 0.058 0.500.500.330.500.500.500.50 1.00 4.000.25

EH9-Nuclear waste rate 0.057 0.500.330.330.503.000.500.50 0.25 1.000.33

EH10-Waste Disposal rate 0.162 0.502.002.003.002.002.003.00 4.00 3.001.00

CR 0.10

Figure 15. Pair-wise matrix for environmental factors.

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Normalised Matrix λMAX λMAX 11

0.210 0.393 0.285 0.148 0.130 0.193 0.15 0.098 0.089 0.320.960 Matrix Size 10

0.052 0.098 0.190 0.222 0.130 0.129 0.1 0.098 0.134 0.081.257 CI 0.15

0.070 0.049 0.095 0.148 0.130 0.129 0.15 0.148 0.134 0.081.191 RI 1.49

0.105 0.032 0.047 0.074 0.130 0.129 0.1 0.098 0.089 0.051.162 CR 0.10

0.105 0.049 0.047 0.037 0.065 0.129 0.15 0.098 0.014 0.081.191

0.070 0.049 0.047 0.037 0.032 0.064 0.1 0.098 0.089 0.081.038

0.070 0.049 0.031 0.037 0.020 0.032 0.05 0.098 0.089 0.051.068

0.105 0.049 0.031 0.037 0.032 0.032 0.025 0.049 0.179 0.041.178

0.105 0.032 0.031 0.037 0.195 0.032 0.025 0.012 0.044 0.051.273

0.105 0.196 0.190 0.222 0.130 0.129 0.15 0.197 0.134 0.161.010

Figure 16. Normalised matrix for environmental factors.

Score C1 C2 C3 C4 C5 C6

C1-Total life-cycle cost ($) 0.347 1.00 2.00 2.00 3.00 5.00 9.00

C2-Material embodied energy cost ($) 0.247 0.50 1.00 2.00 4.00 4.00 3.00

C3-Material capital cost ($) 0.186 0.50 0.50 1.00 2.00 4.00 6.00

C4-Labour/Installation cost ($/sqft) 0.120 0.33 0.25 0.50 1.00 3.00 5.00

C5-Material replacement cost ($) 0.063 0.20 0.25 0.25 0.33 1.00 3.00

C6-Material Maintenance cost ($) 0.037 0.11 0.33 0.17 0.20 0.33 1.00

CR 0.07

Figure 17. Pair-wise matrix for economic/cost factors.

Normalised Matrix λMAX λMAX 6

0.378 0.461 0.338 0.284 0.288 0.333 0.919 Matrix Size 6

0.18 0.230 0.338 0.379 0.230 0.111 1.069 CI 0.09

0.18 0.115 0.169 0.189 0.230 0.222 1.101 RI 1.24

0.12 0.057 0.084 0.094 0.173 0.185 1.267 CR 0.07

0.075 0.057 0.042 0.031 0.057 0.111 1.086

0.042 0.076 0.028 0.018 0.019 0.037 1.001

Figure 18. Normalised matrix for economic/cost factors.

Score SC1 SC2 SC3 SC4 SC5

SC1-Material compatibility with traditions 0.164 1.00 2.00 0.33 0.50 2.00

SC2-Material compatibility with region 0.102 0.50 1.00 0.50 0.50 0.33

SC3-Cultural restriction on usury 0.362 3.00 2.00 1.00 2.00 3.00

SC4-Client’s preference rating 0.227 2.00 2.00 0.50 1.00 2.00

SC5-Conforms to Knowledge of custom 0.146 0.50 3.00 0.33 0.50 1.00

CR 0.08

Figure 19. Pair-wise matrix for socio-cultural factors.

Normalised Matrix λMAX λMAX 5

0.142 0.2 0.125 0.111 0.24 1.147 Matrix Size 5

0.071 0.1 0.187 0.111 0.04 1.020 CI 0.09

0.428 0.2 0.375 0.444 0.36 0.964 RI 1.12

0.285 0.2 0.1875 0.222 0.24 1.022 CR 0.08

0.071 0.3 0.125 0.111 0.12 1.213

Figure 20. Normalised matrix for socio-cultural factors.

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Score T1T2 T3 T4 T5T6T7T8T9T10T11T12T13 T14 T15 T16T17 T17

T1-Recyclable 0.09 1.002.00 2.00 3.00 0.502.002.000.500.502.003.002.002.00 2.00 3.00 0.500.330.50

T2-Ease to remove 0.10 0.501.00 0.33 0.33 0.333.002.003.000.502.003.002.002.00 3.00 2.00 3.002.002.00

T3- Maintenance level 0.06 0.503.00 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T4-Expansion Tolerance 0.06 0.333.00 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T5-Conforms to skills 0.06 2.003.00 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T6-Ease of fixing 0.05 0.500.33 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T7-Fire resistance 0.04 0.500.50 1.00 1.00 1.001.001.001.001.001.001.001.000.11 1.00 0.14 1.001.001.00

T8-Thermal resistance 0.05 2.000.33 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 0.141.001.00

T9-Moisture resistance 0.06 2.002.00 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T10-Scratch resistance 0.05 0.500.50 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T11-Weather resistance 0.05 0.330.33 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T12-Chemical resistance 0.05 0.500.50 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T13-Resistance to decay 0.07 0.500.50 1.00 1.00 1.001.009.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T14-Weight of material 0.05 0.500.33 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T15-Life expectancy 0.07 0.330.50 1.00 1.00 1.001.007.001.001.001.001.001.001.00 1.00 1.00 0.251.001.00

T16-Biodegradable 0.08 2.000.33 1.00 1.00 1.001.001.007.001.001.001.001.001.00 1.00 4.00 1.001.001.00

T17-UV Resistance 0.06 3.000.50 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

T18-Compatibility 0.05 0.501.00 1.00 1.00 1.001.001.001.001.001.001.001.001.00 1.00 1.00 1.001.001.00

CR 0.09

Figure 21. Pair-wise matrix for technical factors.

Normalised Matrix λMAX λMAX 21

0.05 0.11 0.11 0.17 0.02 0.11 0.11 0.02 0.020.110.170.110.110.110.170.020.01 0.02 1.602 Size 18

0.02 0.05 0.01 0.01 0.01 0.17 0.11 0.17 0.020.110.170.110.110.170.110.170.11 0.11 1.778 CI 0.15

0.02 0.17 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 1.083 RI 1.69

0.01 0.17 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 1.074 CR 0.09

0.11 0.17 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 1.167

0.02 0.01 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 0.935

0.02 0.02 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.000.050.010.050.05 0.05 0.847

0.11 0.01 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.010.05 0.05 0.971

0.11 0.11 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 1.111

0.02 0.02 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 0.944

0.01 0.01 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 0.926

0.02 0.02 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 0.944

0.02 0.02 0.05 0.05 0.05 0.05 0.51 0.05 0.050.050.050.050.050.050.050.050.05 0.05 1.389

0.02 0.01 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 0.935

0.01 0.02 0.05 0.05 0.05 0.05 0.4 0.05 0.050.050.050.050.050.050.050.010.05 0.05 1.227

0.11 0.01 0.05 0.05 0.05 0.05 0.05 0.4 0.050.050.050.050.050.050.220.050.05 0.05 1.519

0.17 0.02 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 1.083

0.02 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.050.050.050.050.050.050.050.050.05 0.05 0.972

Figure 22. Normalised matrix for technical factors.

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

Open Access JBCPR

114

Score SN1 SN2 SN3 SN4 SN5 SN6 SN7 SN8 SN9SN10 SN11 SN12 SN13

SN1-Aesthetics 0.077 1.00 1 1 1 1 1 1 1 1 1 1 1 1

SN2-Texture 0.077 1.00 1.00 1 1 1 1 1 1 1 1 1 1 1

SN3-Colour 0.077 1.00 1.00 1.001 1 1 1 1 1 1 1 1 1

SN4-Temperature 0.077 1.00 1.00 1.001.001 1 1 1 1 1 1 1 1

SN5-Acoustics 0.106 1.00 1.00 1.001.001.002 0 4 0 2 0 2 2

SN6-Odour 0.087 1.00 1.00 1.001.000.501.002 1 0 2 1 2 2

SN7-Thickness/Thinness 0.107 1.00 1.00 1.001.003.000.501.002 2 2 3 0 0

SN8-Glossiness/fineness 0.075 1.00 1.00 1.001.000.252.000.501.001 1 1 1 1

SN9-Strength/Hardness 0.109 1.00 1.00 1.001.003.005.000.501.001.001 1 1 1

SN10-Lighting effect 0.068 1.00 1.00 1.001.000.500.500.501.001.001.00 1 1 1

SN11-Translucence 0.108 1.00 1.00 1.001.006.002.000.331.001.001.00 1.00 1 1

SN12-Structure 0.089 1.00 1.00 1.001.000.500.504.001.001.001.00 1.00 1.00 1

SN13-Thermal 0.083 1.00 1.00 1.001.000.500.503.001.001.001.00 1.00 1.00 1.00

CR 0.10

Figure 23. Pair-wise matrix for sensorial factors.

Normalised Matrix λMAX λMAX 15

0.076 0.076 0.076 0.076 0.076 0.076 0.0760.0760.0760.0760.0760.0760.076 1.000 Matrix Size13

0.076 0.076 0.076 0.076 0.076 0.076 0.0760.0760.0760.0760.0760.0760.076 1.000 CI 0.15

0.076 0.076 0.076 0.076 0.076 0.076 0.0760.0760.0760.0760.0760.0760.076 1.000 RI 1.5551

0.076 0.076 0.076 0.076 0.076 0.076 0.0760.0760.0760.0760.0760.0760.076 1.000 CR 0.10

0.076 0.076 0.076 0.076 0.076 0.153 0.0250.3070.0250.1530.0120.1530.153 1.372

0.076 0.076 0.076 0.076 0.038 0.076 0.1530.0380.0150.1530.0380.1530.153 1.131

0.076 0.076 0.076 0.076 0.230 0.038 0.0760.1530.1530.1530.2300.0190.025 1.391

0.076 0.076 0.076 0.076 0.019 0.153 0.0380.0760.0760.0760.0760.0760.076 0.981

0.076 0.076 0.076 0.076 0.230 0.384 0.0380.0760.0760.0760.0760.0760.076 1.423

0.076 0.076 0.076 0.076 0.038 0.038 0.0380.0760.0760.0760.0760.0760.076 0.885

0.076 0.076 0.076 0.076 0.461 0.153 0.0250.0760.0760.0760.0760.0760.076 1.410

0.076 0.076 0.076 0.076 0.038 0.038 0.3070.0760.0760.0760.0760.0760.076 1.154

0.076 0.076 0.076 0.076 0.038 0.038 0.2300.0760.0760.0760.0760.0760.076 1.077

Figure 24. Normalised matrix for sensorial factors.

Using the priorities determined through these matrices,

the weighted overall priority of each candidate material

was determined. The amalgamation method yielded a

single green utility index of alternative worth, which al-

lowed the material options to be ranked according to

their overall priorities. The material with the highest

score then becomes the selected candidate material as

shown in Figure 38. Looking at Figure 38, it is clear

from the results of the analysis that Material option (A)

turns out to be the most preferred material among the

three material options identified in Table 4, with an

overall priority or index score of 0.086. It is based on the

concept of the higher the green utility index value, the

better the option. The green utility index as calculated for

each of the three material alternatives was M(C) = 0.086,

M(A) = 0.072 and M(B) = 0.062 for material options C,

A and B respectively, making Option C (fly-ash cement

concrete floor slab) emerge as the best option amongst

the other alternatives as shown in Figure 38.

The above example has illustrated the application of

the MSDSS in a material selection problem for a pro-

posed 5-bedroom low-cost residential green building

project in the London Borough of Sutton. From the illus-

trated example it can be deduced that the MSDSS model

is able to provide rankings in low-cost green building

material assessment combining site, economic, technical,

social-cultural, sensorial and environmental criteria into a

composite index system based on the AHP technique.

This model is therefore, based on the presumption that

decision makers, given full knowledge of all possible

consequences of all possible alternatives and factors, will

select the material with the highest-ranking score.

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

Open Access JBCPR

115

GS1-Location (km) CSR CP RL B.XL FA RT FPH. SS RPB T&GW PB T&G SC SIT

Compressed Stabilized

Rammed Earth blocks 1.0 2.0 2.0 4.02.05.08.08.04.04.00 4.0 4.00 7.0 2.004.0

Clay Products-Unfired Bricks 0.5 1.0 1.0 3.01.04.07.07.03.03.00 3.0 3.00 6.0 1.003.0

Reclaimed/Recycled laminated

Wood Flooring and Panelling 0.5 1.0 1.0 3.01.04.07.07.03.03.00 3.0 3.00 6.0 1.003.0

Bamboo XL laminated Split

Paneled Flooring 0.3 0.3 0.3 1.00.32.05.05.01.01.0 1.0 1.00 4.0 0.331.0

Fly Ash Sand Lime

interlocking Paving

Bricks/Block

0.5 1.0 1.0 3.01.04.007.007.003.03.00 3.0 3.0 6.0 1.03.0

Recycled timber clad

Aluminium framed window

unit

0.2 0.3 0.3 0.50.31.04.004.000.500.50 0.5 0.50 3.0 0.30.5

Four panel hardwood door

finished with Alpilignum. 0.1 0.1 0.1 0.20.10.31.01.00.20.2 0.2 0.2 0.5 0.10.2

Stainless Steel Entry Door. 0.1 0.1 0.1 0.20.10.31.01.00.20.2 0.2 0.2 0.5 0.10.20

Reprocessed Particleboard

wood chipboard to BS EN 312

Type P5,

0.3 0.3 0.3 1.00.32.05.05.01.01.00 1.0 1.00 4.0 0.31.00

Tongue & grooved Wooddeco

Multiline ceiling tiles to BS EN

636–2]

0.3 0.3 0.3 1.00.32.05.05.01.01.00 1.0 1.00 4.0 0.331.00

Plasterboard on 70 mm steel

studs with 50 mm 12.9 kg/m3

insulation,

0.3 0.3 0.3 1.00.32.05.05.01.01.00 1.0 1.00 4.00 0.331.00

Tongue & Grooved Laminated

Wooden column bolted to steel

plate on concrete base.

0.3 0.3 0.3 1.00.32.05.05.01.01.0 1.0 1.00 4.0 0.331.0

Steel Column UC 0.1 0.2 0.2 0.30.20.332.002.000.30.25 0.3 0.3 1.0 0.170.3

Structurally insulated timber

panel system with OSB/3 each

side, roofing underlay

reclaimed clay tiles

0.5 1.0 1.0 3.01.04.07.07.03.03.0 3.0 3.0 6.0 1.03.0

Structurally insulated natural

slate (temperate EN 636-2)

decking each side]

0.3 0.3 0.3 1.00.32.05.005.001.001.00 1.0 1.00 4.0 0.31.0

Total 5.1 8.7 8.7 23.28.734.874.074.023.223.2 23.2 23.2 60.0 8.723.2

Figure 25. Pair-wise matrix: location.

CS CP RL B.XL FA RT FPH. SS. RP, T&G] PB T&GW. SC SIT SIS

0.2 0.2 0.2 0.2 0.23 0.14 0.11 0.11 0.17 0.170.170.17 0.12 0.23 0.17

0.1 0.1 0.1 0.1 0.11 0.11 0.09 0.09 0.13 0.130.130.13 0.10 0.11 0.13

0.0 0.0 0.0 0.0 0.04 0.1 0.07 0.07 0.04 0.040.040.04 0.07 0.04 0.04

0.10 0.11 0.11 0.13 0.11 0.11 0.09 9.46E-02 0.13 0.130.130.13 0.10 0.11 0.13

0.0 0.0 0.03 0.02 0.03 0.03 0.05 0.05 0.02 0.020.020.02 0.05 0.03 0.02

0.0 0.0 0.0 0.0 0.02 0.0 0.01 0.0135134 0.01 0.010.010.01 0.01 0.02 0.01

0.0 0.0 0.0 0.0 0.02 0.0 0.01 4 0.01 0.010.010.01 0.01 0.02 0.01

0.0 0.0 0.0 0.0 0.04 0.1 0.07 0.0675675680.04 0.040.040.04 0.07 0.04 0.04

0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0675675680.04 0.040.040.04 0.07 0.04 0.04

0.05 0.04 0.04 0.0 0.04 0.1 0.1 0.0675675680.04 0.040.040.04 0.07 0.04 0.04

0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0675675680.04 0.040.040.04 0.07 0.04 0.04

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0270270270.01 0.010.010.01 0.02 0.02 0.01

0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0945945950.13 0.130.130.13 0.10 0.11 0.13

0.0 0.0 0.0 0.0 0.04 0.1 0.07 0.0675675680.04 0.040.040.04 0.07 0.04 0.04

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.001.00 1.00 1.00 1.00

Figure 26. Normalised matrix: location.

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

Open Access JBCPR

116

EH2-Embodied CO2 Emission (KgCO2/m2)

Compressed Stabilized Rammed Earth blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood Flooring and

Panelling

Bamboo XL laminated Split Paneled Flooring

Fly Ash Sand Lime interlocking Paving

Bricks/Block

Recycled timber clad Aluminium framed

window unit

Four panel hardwood door finished with

Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood chipboard to BS

EN 312 Type P5,

Tongue & grooved Wooddeco Multiline ceiling tiles

to BS EN 636–2]

Plasterboard on 70 mm steel studs with 50 mm

12.9 kg/m3 insulation,

Tongue & Grooved Laminated Wooden column

bolted to steel plate on concrete base.

Steel Column UC

Structurally insulated timber panel system with

OSB/3 each side, roofing underlay reclaimed

clay tiles

Structurally insulated natural slate (temperate EN

636-2) decking each side]

Compressed Stabilized

Rammed Earth blocks 1.01.0 5.0 1.01.0 5.0 5.0 8.02.01.04.0 5.00 6.00 5.00 1.00

Clay Products—Unfired

Bricks 1.01.0 5.0 1.01.0 5.0 5.0 8.02.01.04.0 5.0 6.0 5.0 1.0

Reclaimed/Recycled

laminated Wood Flooring

and Panelling

0.20.2 1.0 0.20.2 1.0 1.0 4.00.30.20.5 1.0 2.0 1.0 0.2

Bamboo XL laminated Split

Paneled Flooring 1.01.0 5.0 1.01.0 5.0 5.0 8.02.01.04.0 5.0 6.0 5.0 1.0

Fly Ash Sand Lime

interlocking Paving

Bricks/Block

1.01.0 5.0 1.01.0 5.0 5.0 8.02.01.04.0 5.0 6.0 5.0 1.0

Recycled timber clad

Aluminium framed window

unit

0.20.2 1.0 0.20.2 1.0 1.0 4.00.30.20.5 1.0 2.0 1.0 0.2

Four panel hardwood door

finished with Alpilignum. 0.20.2 1.0 0.20.2 1.0 1.0 4.00.30.20.5 1.0 2.0 1.0 0.2

Stainless Steel Entry Door. 0.1250.125 0.25 0.1250.1250.250.2510.140.1250.2 0.25 0.3 0.25 0.125

Reprocessed Particleboard

wood chipboard to BS EN

312 Type P5,

0.50.5 4.0 0.50.5 4.0 4.0 7.01.00.53.0 4.0 5.0 4.0 0.5

Tongue & grooved

Wooddeco Multiline ceiling

tiles to BS EN 636–2]

1 1 5 1 1 5 5 82 1 4 5 6 5 1

Plasterboard on 70 mm steel

studs with 50 mm 12.9 kg/m3

insulation,

0.250.25 2 0.250.25 2 2 50.30.251 2 3 2 0.25

Tongue & Grooved

Laminated Wooden column

bolted to steel plate on

concrete base.

0.200.20 1.00 0.200.20 1.001.004.000.250.200.50 1.00 2.00 1.00 0.20

Steel Column UC 0.20.2 0.5 0.20.2 0.5 0.5 3.00.20.20.3 0.5 1.0 0.5 0.2

Structurally insulated timber

panel system with OSB/3

each side, roofing underlay

reclaimed clay tiles

0.20.2 1.0 0.20.2 1.0 1.0 4.00.30.20.5 1.0 2.0 1.0 0.2

Structurally insulated

natural slate (temperate EN

636-2) decking each side]

1.01.0 5.0 1.01.0 5.0 5.0 8.02.01.04.0 5.00 6.00 5.00 1.00

Total 8.08.0 41.88.08.041.841.884.0 14.9 8.0

Figure 27. Pair-wise matrix: embodied CO2 emissions.

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

Open Access JBCPR

117

Compressed Stabilized Rammed Earth blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood Flooring and

Panelling

Bamboo XL laminated Split Paneled Flooring

Fly Ash Sand Lime interlocking Paving Bricks/Block

Recycled timber clad Aluminium framed window unit

Four panel hardwood door finished with Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood chipboard to BS EN 312

Type P5,

Tongue & grooved Wooddeco Multiline ceiling tiles to

BS EN 636–2]

Plasterboard on 70 mm steel studs with 50 mm

12.9 kg/m3 insulation,

Tongue & Grooved Laminated Wooden column bolted to

steel plate on concrete base.

Steel Column UC

Structurally insulated timber panel system with OSB/3

each side, roofing underlay reclaimed clay tiles

Structurally insulated natural slate (temperate EN 636-2)

decking each side]

0.03

0.12 0.12 0.12 0.1 0.12 0.10.10.10.120.12 0.130.120.11 0.12 0.12 0.12 0.97RI 1.58

0.1 0.1 0.1 0.1 0.1 0.10.10.10.10.12 0.130.120.11 0.12 0.12 0.12 0.97CR 0.02

0.0 0.0 0.0 0.0 0.0 0.00.00.050.010.02 0.020.020.04 0.02 0.02 0.03 1.07

0.1 0.1 0.1 0.1 0.1 0.10.10.10.120.12 0.130.120.11 0.12 0.12 0.12 0.97

0.1 0.1 0.1 0.1 0.12 0.10.120.10.120.12 0.130.120.11 0.12 0.12 0.12 0.97

0.0 0.0 0.0 0.0 0.02 0.00.020.050.010.02 0.020.020.04 0.02 0.02 0.03 1.07

0.015544041 0.015544041 0.005988024 0.01 0.02 0.0040.010.010.0090.0155440410.030.0050.006 0.005 0.015 0.01 0.88

0.1 0.1 0.1 0.1 0.06 0.10.100.10.060.06 0.100.100.09 0.10 0.06 0.08 1.18

0.124352332 0.124352332 0.119760479 0.12 0.12 0.110.120.10.10.1243523320.120.110.105 0.11 0.122 0.12 0.97

0.031088083 0.031088083 0.047904192 0.03 0.03 0.040.050.10.0210.0310880830.030.0470.057 0.047 0.03 0.04 1.23

0.02 0.02 0.02 0.02 0.02 0.020.020.10.010.0248704660.010.020.038 0.02 0.026 0.03 1.07

0.0 0.0 0.0 0.0 0.02 0.00.010.010.010.02 0.010.010.02 0.01 0.02 0.02 0.97

0.0 0.0 0.0 0.0 0.0 0.00.00.050.010.02 0.020.020.04 0.02 0.02 0.03 1.07

0.12 0.12 0.12 0.1 0.12 0.10.1010.10.12 0.130.120.11 0.12 0.12 0.12 0.97

1.00 1.00 1.00 1.00 1.00 1.001.001.001.001.00 1.001.001.00 1.00 1.00 1.00 15.5

Figure 28. Normalised matrix: embodied CO2 emissions.

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

Open Access JBCPR

118

C1- Total life-cycle cost ($)

Compressed Stabilized Rammed Earth blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood Flooring and Panelling

Bamboo XL laminated Split Paneled Flooring

Fly Ash Sand Lime interlocking Paving Bricks/Block

Recycled timber clad Aluminium framed window unit

Four panel hardwood door finished with Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood chipboard to

BS EN 312 Type P5,

Tongue & grooved Wooddeco Multiline ceiling tiles to

BS EN 636–2]

Plasterboard on 70 mm steel studs with 50 mm 12.9 kg/m3

insulation,

Tongue & Grooved Laminated Wooden column bolted to steel

plate on concrete base.

Steel Column UC

Structurally insulated timber panel system with OSB/3 each

side, roofing underlay reclaimed clay tiles

Structurally insulated natural slate (temperate EN 636-2)

decking each side]

Compressed Stabilized Rammed Earth

blocks 1.0 0.5 3.00.52.07.08.07.07.08.0 8.0 8.0 7.0 7.0 7.0

Clay Products- Unfired Bricks 2 1 4 13 8 9 8 8 9 9 9 8 8 8

Reclaimed/Recycled laminated Wood

Flooring and Panelling 0.3 0.3 1.00.30.55.06.05.05.06.0 6.0 6.00 5.00 5.005.00

Bamboo XL laminated Split Paneled

Flooring 2 1 4 13 8 9 8 8 9 9 9 8 8 8

Fly Ash Sand Lime interlocking Paving

Bricks/Block 0.5 0.3 2 0.31 6 7 6 6 7 7 7 6 6 6

Recycled timber clad Aluminium framed

window unit 0.14 0.13 0.200.130.171.002.001.001.002.002.00 2.00 1.00 1.001.00

Four panel hardwood door finished with

Alpilignum. 0.1 0.1 0.20.10.10.51.00.50.51.0 1.0 1.0 0.5 0.5 0.5

Stainless Steel Entry Door. 0.1 0.1 0.20.10.21.02.01.01.02.0 2.0 2.0 1.0 1.0 1.0

Reprocessed Particleboard wood

chipboard to BS EN 312 Type P5, 0.1 0.1 0.20.10.21.02.01.01.02.0 2.0 2.0 1.0 1.0 1.0

Tongue & grooved Wooddeco Multiline

ceiling tiles to BS EN 636–2] 0.1 0.1 0.20.10.10.51.00.50.51.0 1.0 1.0 0.5 0.5 0.5

Plasterboard on 70 mm steel studs with

50 mm 12.9 kg/m3 insulation, 0.1 0.1 0.20.10.10.51.00.50.51.0 1.0 1.0 0.5 0.5 0.5

Tongue & Grooved Laminated Wooden

column bolted to steel plate on concrete

base.

0.1 0.1 0.20.10.10.51.00.50.51.0 1.0 1.0 0.5 0.5 0.5

Steel Column UC 0.1 0.1 0.20.10.21.02.01.01.02.0 2.0 2.0 1.0 1.0 1.0

Structurally insulated timber panel

system with OSB/3 each side, roofing

underlay reclaimed clay tiles

0.1 0.1 0.20.10.21.02.01.01.02.0 2.0 2.0 1.0 1.0 1.0

Structurally insulated natural slate

(temperate EN 636-2) decking each side] 0.1 0.1 0.20.10.21.02.01.01.02.0 2.0 2.0 1.0 1.0 1.0

Total 7.2 4.3 15.94.311.142.055.042.042.055.055.0 55.0 42.0 42.042.0

Figure 29. Pair-wise matrix: total life-cycle cost.

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119

Compressed Stabilized Rammed Earth blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood Flooring and

Panelling

Bamboo XL laminated Split Paneled Flooring

Fly Ash Sand Lime interlocking Paving Bricks/Block

Recycled timber clad Aluminium framed window unit

Four panel hardwood door finished with Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood chipboard to BS EN

312 Type P5,

Tongue & grooved Wooddeco Multiline ceiling tiles to

BS EN 636–2]

Plasterboard on 70 mm steel studs with 50 mm

12.9 kg/m3 insulation,

Tongue & Grooved Laminated Wooden column bolted

to steel plate on concrete base.

Steel Column UC

Structurally insulated timber panel system with OSB/3

each side, roofing underlay reclaimed clay tiles

Structurally insulated natural slate (temperate EN 636-2)

decking each side]

Average

Lambda Max

0.06

0.1 0.1 0.2 0.1 0.18 0.2 0.150.17 0.1667 0.15 0.15 0.15 0.17 0.17 0.17 0.15 1.11RI1.58

0.2 0.2 0.2 0.2 0.2 0.19 0.10.19 0.190 0.1636363640.1636363640.1636363640.190476190.19047619 0.19047619 0.20 0.87 CR 0.04

0.05 0.06 0.06 0.1 0.05 0.1 0.10.19 0.119 0.11 0.11 0.11 0.12 0.12 0.12 0.09 1.50

0.2 0.24 0.25 0.2 0.2 0.19 0.160.1909 0.190 0.1636363640.1636363640.1636363640.190476190.19047619 0.19047619 0.20

0.0 0.08 0.12 0.07 0.09 0.14 0.123 0.14 0.1272727270.1272727270.1272727270.1428571430.142857143 0.142857143 0.12

0.02 0.03 0.01 0.03 0.02 0.02 0.040.024 0.02 0.04 0.0363636360.0363636360.0238095240.023809524 0.023809524 0.03

0.0 0.0 0.0 0.0 0.01 0.0 0.020.0162 0.01 0.02 0.02 0.02 0.01 0.01 0.01 0.02

Figure 30. Normalised matrix: total life-cycle cost.

SC3- Cultural restriction on usury

Compressed Stabilized Rammed Earth blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood Flooring and

Panelling

Bamboo XL laminated Split Paneled Flooring

Fly Ash Sand Lime interlocking Paving Bricks/Block

Recycled timber clad Aluminium framed window unit

Four panel hardwood door finished with Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood chipboard to BS EN

312 Type P5,

Tongue & grooved Wooddeco Multiline ceiling tiles to

BS EN 636–2]

Plasterboard on 70 mm steel studs with 50 mm

12.9 kg/m3 insulation,

Tongue & Grooved Laminated Wooden column bolted

to steel plate on concrete base.

Steel Column UC

Structurally insulated timber panel system with OSB/3

each side, roofing underlay reclaimed clay tiles

Structurally insulated natural slate (temperate EN

636-2) decking each side]

Compressed Stabilized Rammed

Earth blocks 1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

Clay Products—Unfired Bricks 1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

Reclaimed/Recycled laminated Wood

Flooring and Panelling 1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

Bamboo XL laminated Split Paneled

Flooring 1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

Fly Ash Sand Lime interlocking

Paving Bricks/Block 1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

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Continued

Recycled timber clad Aluminium

framed window unit 3.0 3.0 3.0 3.03.01.01.00.33.0 3.0 1.0 3.0 0.2 3.0 3.0

Four panel hardwood door finished

with Alpilignum. 3.0 3.0 3.0 3.03.01.01.00.33.0 3.0 1.0 3.0 0.2 3.0 3.0

Stainless Steel Entry Door. 5.0 5.0 5.0 5.05.03.03.01.05.0 5.0 3.0 5.0 0.3 5.0 5.0

Reprocessed Particleboard wood

chipboard to BS EN 312 Type P5, 1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

Tongue & grooved Wooddeco

Multiline ceiling tiles to BS EN 636–2] 1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

Plasterboard on 70 mm steel studs

with 50 mm 12.9 kg/m3 insulation, 3.0 3.0 3.0 3.03.01.01.00.33.0 3.0 1.0 3.00 0.20 3.003.00

Tongue & Grooved Laminated

Wooden column bolted to steel plate

on concrete base.

1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

Steel Column UC 7.0 7.0 7.0 7.07.05.05.03.07.0 7.0 5.0 7.0 1.0 7.0 7.0

Structurally insulated timber panel

system with OSB/3 each side, roofing

underlay reclaimed clay tiles

1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

Structurally insulated natural slate

(temperate EN 636-2) decking each

side]

1.0 1.0 1.0 1.01.00.30.30.21.0 1.0 0.3 1.0 0.1 1.0 1.0

Total 31.0 31.0 31.031.031.014.314.37.031.031.014.3 31.0 3.4 31.031.0

Figure 31. Pair-wise matrix: cultural restriction on usury.

Compressed Stabilized Rammed Earth blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood Flooring

and Panelling

Bamboo XL laminated Split Paneled Flooring

Fly Ash Sand Lime interlocking Paving

Bricks/Block

Recycled timber clad Aluminium framed

window unit

Four panel hardwood door finished with

Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood chipboard to

BS EN 312 Type P5,

Tongue & grooved Wooddeco Multiline ceiling

tiles to BS EN 636–2]

Plasterboard on 70 mm steel studs with 50 mm

12.9 kg/m3 insulation,

Tongue & Grooved Laminated Wooden column

bolted to steel plate on concrete base.

Steel Column UC

Structurally insulated timber panel system with

OSB/3 each side, roofing underlay reclaimed

clay tiles

Structurally insulated natural slate (temperate

EN 636-2) decking each side]

Average

Lambda Max

0.02

0.0 0.0 0.0 0.0 0.03 0.0 0.02 0.02 0.030.030.020.030.040.03 0.03 0.03 0.96RI 1.58

0.0 0.0 0.0 0.0 0.03 0.0 0.02 0.02 0.030.030.020.030.040.03 0.03 0.03 0.96CR 0.01

0.0 0.0 0.0 0.0 0.03 0.0 0.02 0.02 0.030.030.020.030.040.03 0.03 0.03 0.96

0.1 0.1 0.1 0.1 0.10 0.1 0.07 0.04 0.090.100.070.100.060.10 0.10 0.09 1.23

0.2 0.2 0.2 0.2 0.16 0.2 0.21 0.14 0.160.160.210.160.100.16 0.16 0.17 1.16

0.0 0.0 0.0 0.0 0.03 0.0 0.02 0.02 0.030.030.020.030.040.03 0.03 0.03 0.96

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.02 0.030.030.020.030.040.03 0.03 0.03 0.96

0.10 0.10 0.10 0.1 0.10 0.1 0.1 0.04 0.090.100.070.100.060.10 0.10 0.09 1.23

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0280.030.030.020.030.040.03 0.03 0.03 0.96

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Continued

0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.42 0.220.230.350.230.300.23 0.23 0.27 0.90

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.02 0.030.030.020.030.040.03 0.03 0.03 0.96

0.0 0.0 0.0 0.0 0.03 0.0 0.02 0.02 0.030.030.020.030.040.03 0.03 0.03 0.96

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.001.001.001.001.00 1.00 1.00 15.3

Figure 32. Normalised matrix: cultural restriction on usury.

T2-Ease to remove/reaffix/replace

Compressed Stabilized Rammed Earth blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood Flooring

and Panelling

Bamboo XL laminated Split Paneled Flooring

Fly Ash Sand Lime interlocking Paving

Bricks/Block

Recycled timber clad Aluminium framed window

unit

Four panel hardwood door finished with

Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood chipboard to BS

EN 312 Type P5,

Tongue & grooved Wooddeco Multiline ceiling

tiles to BS EN 636–2]

Plasterboard on 70 mm steel studs with 50 mm

12.9 kg/m3 insulation,

Tongue & Grooved Laminated Wooden column

bolted to steel plate on concrete base.

Steel Column UC

Structurally insulated timber panel system with

OSB/3 each side, roofing underlay reclaimed clay

tiles

Structurally insulated natural slate (temperate EN

636-2) decking each side]

Compressed Stabilized Rammed

Earth blocks 1.0 0.3 0.20.20.3 0.20.20.30.20.20.3 0.20 0.33 0.200.20

Clay Products—Unfired Bricks 3.0 1.0 0.30.30.5 0.30.31.00.30.31.0 0.3 1.0 0.3 0.3

Reclaimed/Recycled laminated Wood

Flooring and Panelling 5.0 3.0 1.01.02.0 1.01.03.01.01.03.0 1.0 3.0 1.0 1.0

Bamboo XL laminated Split Paneled

Flooring 5.0 3.0 1.01.02.0 1.01.03.01.01.03.0 1.0 3.0 1.0 1.0

Fly Ash Sand Lime interlocking

Paving Bricks/Block 4.0 2.0 0.50.51.0 0.50.52.00.50.52.0 0.5 2.0 0.5 0.5

Recycled timber clad Aluminium

framed window unit 5.0 3.0 1.01.02.0 1.01.03.01.01.03.0 1.0 3.0 1.0 1.0

Four panel hardwood door finished

with Alpilignum. 5.0 3.0 1.01.02.0 1.01.03.01.01.03.0 1.0 3.0 1.0 1.0

Stainless Steel Entry Door. 3.0 1.0 0.30.30.5 0.30.31.00.30.31.0 0.3 1.0 0.3 0.3

Reprocessed Particleboard wood

chipboard to BS EN 312 Type P5, 5.0 3.0 1.01.02.0 1.01.03.01.01.03.0 1.0 3.0 1.0 1.0

Tongue & grooved Wooddeco

Multiline ceiling tiles to BS EN 636–2] 5.0 3.0 1.01.02.0 1.01.03.01.01.03.0 1.0 3.0 1.0 1.0

Plasterboard on 70 mm steel studs

with 50 mm 12.9kg/m3 insulation, 3.0 1.0 0.30.30.5 0.30.31.00.30.31.0 0.3 1.0 0.3 0.3

Tongue & Grooved Laminated

Wooden column bolted to steel plate

on concrete base.

5.0 3.0 1.01.02.0 1.01.03.01.01.03.0 1.0 3.0 1.0 1.0

Steel Column UC 3.0 1.0 0.30.30.5 0.30.31.00.30.31.0 0.3 1.0 0.3 0.3

Structurally insulated timber panel

system with OSB/3 each side, roofing

underlay reclaimed clay tiles

5.0 3.0 1.01.02.0 1.01.03.01.01.03.0 1.0 3.0 1.0 1.0

Structurally insulated natural slate

(temperate EN 636-2) decking each

side]

5.0 3.0 1.01.02.0 1.01.03.01.01.03.0 1.00 3.00 1.001.00

Total 62.0 33.3 11.011.021.311.011.033.311.011.033.3 11.0 33.3 11.011.0

Figure 33. Pair-wise matrix: ease to remove/affix/replace.

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

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122

Compressed Stabilized Rammed Earth

blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood

Flooring and Panelling

Bamboo XL laminated Split Paneled

Flooring

Fly Ash Sand Lime interlocking

Paving Bricks/Block

Recycled timber clad Aluminium

framed window unit

Four panel hardwood door finished

with Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood

chipboard to BS EN 312 Type P5,

Tongue & grooved Wooddeco

Multiline ceiling tiles to BS EN 636–2]

Plasterboard on 70 mm steel studs with

50 mm 12.9 kg/m3 insulation,

Tongue & Grooved Laminated

Wooden column bolted to steel plate

on concrete base.

Steel Column UC

Structurally insulated timber panel sys-

tem with OSB/3 each side, roofing

underlay reclaimed clay tiles

Structurally insulated natural slate

(temperate EN 636-2) decking each

side]

Average

Lambda Max

0.01

0.02 0.01 0.02 0.0 0.01 0.0 0.0 0.01 0.010.020.010.02 0.010.02 0.02 0.02 0.95RI

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03 0.030.030.030.03 0.030.03 0.03 0.03 1.03CR

0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.09 0.090.090.090.09 0.090.09 0.09 0.09 0.99

0.1 0.1 0.0 0.0 0.05 0.0 0.05 0.06 0.040.050.060.05 0.060.05 0.05 0.05 1.08

0.1 0.1 0.1 0.1 0.09 0.1 0.09 0.09 0.090.090.090.09 0.090.09 0.09 0.09 0.99

0.0 0.0 0.0 0.0 0.02 0.0 0.03 0.03 0.030.030.030.03 0.030.03 0.03 0.03 1.03

0.1 0.1 0.1 0.1 0.09 0.1 0.09 0.09 0.090.090.090.09 0.090.09 0.09 0.09 0.99

0.0 0.0 0.0 0.0 0.02 0.0 0.03 0.03 0.030.030.030.03 0.030.03 0.03 0.03 1.03

0.1 0.1 0.1 0.1 0.09 0.1 0.09 0.09 0.090.090.090.09 0.090.09 0.09 0.09 0.99

0.0 0.0 0.0 0.0 0.02 0.0 0.03 0.03 0.030.030.030.03 0.030.03 0.03 0.03 1.03

0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.09 0.090.090.090.09 0.090.09 0.09 0.09 0.99

0.08 0.09 0.09 0.1 0.09 0.1 0.1 0.09 0.090.090.090.09 0.090.09 0.09 0.09 0.99

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.001.001.00 1.001.00 1.00 1.00 15.1

Figure 34. Normalised matrix: ease to remove/affix/replace.

SN5- Acoustics Performance

Compressed Stabilized Rammed Earth blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood Flooring and Panelling

Bamboo XL laminated Split Paneled Flooring

Fly Ash Sand Lime interlocking Paving Bricks/Block

Recycled timber clad Aluminium framed window unit

Four panel hardwood door finished with Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood chipboard to BS EN 312

Type P5,

Tongue & grooved Wooddeco Multiline ceiling tiles to BS EN

636–2]

Plasterboard on 70 mm steel studs with 50 mm 12.9 kg/m3

insulation,

Tongue & Grooved Laminated Wooden column bolted to steel

plate on concrete base.

Steel Column UC

Structurally insulated timber panel system with OSB/3 each

side, roofing underlay reclaimed clay tiles

Structurally insulated natural slate (temperate EN 636-2)

decking each side]

Compressed Stabilized Rammed

Earth blocks 1.0 0.2 0.30.20.20.30.21.00.30.30.3 0.3 0.3 0.31.0

Clay Products—Unfired Bricks 5.0 1.0 2.01.01.03.01.05.02.02.02.0 2.0 2.0 2.05.0

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Continued

Reclaimed/Recycled laminated Wood

Flooring and Panelling 4.0 0.5 1.00.50.52.00.54.01.01.01.0 1.0 1.0 1.04.0

Bamboo XL laminated Split Paneled

Flooring 5.0 1.0 2.01.01.03.01.05.02.02.02.0 2.0 2.0 2.05.0

Fly Ash Sand Lime interlocking

Paving Bricks/Block 5.0 1.0 2.01.01.03.01.05.02.02.02.0 2.0 2.0 2.05.0

Recycled timber clad Aluminium

framed window unit 3.0 0.3 0.50.30.31.00.33.00.50.50.5 0.5 0.5 0.53.0

Four panel hardwood door finished

with Alpilignum. 5.0 1.0 2.01.01.03.01.05.02.02.02.0 2.0 2.0 2.05.0

Stainless Steel Entry Door. 1.0 0.2 0.30.20.20.30.21.00.30.30.3 0.3 0.3 0.31.0

Reprocessed Particleboard wood

chipboard to BS EN 312 Type P5, 4 0.5 1 0.50.520.5 41 1 1 1 1 1 4

Tongue & grooved Wooddeco

Multiline ceiling tiles to BS EN 636–2] 4.0 0.5 1.00.50.52.00.54.01.01.01.0 1.0 1.0 1.04.0

Plasterboard on 70 mm steel studs

with 50 mm 12.9 kg/m3 insulation, 4 0.5 1 0.50.520.5 41 1 1 1 1 1 4

Tongue & Grooved Laminated

Wooden column bolted to steel plate

on concrete base.

4 0.5 1 0.50.520.5 41 1 1 1 1 1 4

Steel Column UC 4.00 0.50 1.000.500.502.000.504.001.001.001.00 1.00 1.00 1.004.00

Structurally insulated timber panel

system with OSB/3 each side, roofing

underlay reclaimed clay tiles

4.0 0.5 1.00.50.52.00.54.01.01.01.0 1.0 1.0 1.04.0

Structurally insulated natural slate

(temperate EN 636-2) decking each

side]

1.0 0.2 0.30.20.20.30.21.00.30.30.3 0.3 0.3 0.31.0

Total 54.0 8.4 16.38.48.428.08.454.016.316.316.3 16.3 16.3 16.354.0

Figure 35. Pair-wise matrix: acoustics performance.

Compressed Stabilized Rammed Earth

blocks

Clay Products- Unfired Bricks

Reclaimed/Recycled laminated Wood

Flooring and Panelling

Bamboo XL laminated Split Paneled

Flooring

Fly Ash Sand Lime interlocking Paving

Bricks/Block

Recycled timber clad Aluminium framed

window unit

Four panel hardwood door finished with

Alpilignum.

Stainless Steel Entry Door.

Reprocessed Particleboard wood

chipboard to BS EN 312 Type P5,

Tongue & grooved Wooddeco Multiline

ceiling tiles to BS EN 636–2]

Plasterboard on 70 mm steel studs with

50 mm 12.9 kg/m3 insulation,

Tongue & Grooved Laminated Wooden

column bolted to steel plate on concrete

base.

Steel Column UC

Structurally insulated timber panel system

with OSB/3 each side, roofing underlay

reclaimed clay tiles

Structurally insulated natural slate

(temperate EN 636-2) decking each side]

Average

Lambda Max

0.01

0.0 0.0 0.0 0.0 0.0 0.00.0 0.01 0.010.020.020.02 0.020.02 0.02 0.02 0.97RI1.58

0.1 0.1 0.1 0.1 0.1 0.10.1 0.09 0.120.120.120.12 0.120.12 0.09 0.11 0.97CR0.01

0.1 0.1 0.1 0.1 0.1 0.10.1 0.07 0.0610.060.060.06 0.060.06 0.07 0.06 1.04

0.1 0.1 0.1 0.1 0.1 0.10.1 0.09 0.1230.120.120.12 0.120.12 0.09 0.11 0.97

0.1 0.0 0.0 0.0 0.0 0.00.0 0.05 0.0300.030.030.03 0.030.03 0.06 0.04 1.07

0.1 0.1 0.1 0.1 0.1 0.10.1 0.09 0.1230.120.120.12 0.120.12 0.09 0.11 0.97

0.0 0.0 0.0 0.0 0.0 0.00.0 0.018 0.010.020.020.02 0.020.02 0.02 0.02 0.97

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Continued

0.074 0.059 0.06 0.059 0.059 0.071 0.059 0.074 0.061 0.0610.0610.0615384620.620.061538462 0.074074074 0.06 1.04

0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.074 0.061 0.060.060.06 0.060.06 0.07 0.06 1.04

0.07 0.059 0.06 0.059 0.059 0.071 0.059 0.074 0.061 0.0610.0610.06153846204620.061538462 0.074074074 0.06 1.04

0.07 0.059 0.06 0.059 0.059 0.071 0.059 0.074 0.061 0.0610.0610.0615384620.00.061538462 0.074074074 0.06 1.04

0.07 0.06 0.06 0.06 0.06 0.07 0.06 0.074 0.06 0.0610.0610.061538462620.061538462 0.074074074 0.06 1.04

0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.074 0.061 0.060.060.06 0.060.06 0.07 0.06 1.04

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018518519 0.0153846150.020.020.02 0.020.02 0.02 0.02 0.97

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.001.00 1.001.00 1.00 1.00 15.2

Figure 36. Normalised matrix: acoustics performance.

Figure 37. Green utility indices of the selected materials.

6. Potential Benefits of the MSDSS Model

The following are the benefits expected from the applica-

tion of the MSDSS Model. However the model devel-

oped for this research differs from that of the previous

works in the following ways:

• The main point of difference from the off-the-shelf

assessment tools is that they only trade-off numerical

values based on the single-attributes. These single-

attribute claims ignore the possibility of what other

variables can yield. MSDSS supports trade-off with

and without tangible variables, such as a client’s

preference, environmental statutory compliance, and

cultural restriction on usury. This feature is important

as decision making in reality engages with solid, ver-

bal and subjective elements.

• In terms of cost, it provides an opportunity for de-

signers to be able to advise their clients as to what the

probable financial estimate of the project may be.

This helps clients to decide how much they are pre-

pared to spend on different variables of construction.

• A separate set of contextual considerations was in-

cluded as a heuristics base to facilitate site-specific

feasibility and appropriateness testing of each mate-

rial choice. Boundaries of sustainability inform of

knowledge base rules as contained in the MSDSS

model could help reduce bias that is often associated

with the material selection process.

• Available material assessment tools are particularity

ill-adapted for the early stages of the design process

and are generally labour intensive. The MSDSS

model consists of a resource for relatively small

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

Open Access JBCPR

125

Figure 38. Corresponding indices of the ranked materials.

information input to produce quick and fairly accurate

or approximate output of results with little or no

training on the part of experienced users. This means

that users that may require little training are inexpe-

rienced users but not as extensive as obtainable in

previous tools.

• There are still significant numbers of smaller firms

who cannot afford most material assessment tools

because they are extremely expensive. This tool is

more or less open source software recommended to

provide solution to this challenge.

• Context is a critical consideration for all project deci-

sion-making, since even projects located on neigh-

bouring sites will have different end users, and dif-

ferent specific site characteristics. This tool could be

applied to other regions with minimal or no changes,

and therefore has the ability to adapt to any situation,

or change in design according to users’ needs or dif-

ferent material alternatives.

• Unlike in the previous models, this tool contains tuto-

rials and help menu as well as video guidance on how

to use the software. This provides adequate help to

beginners or inexperienced designers.

• For the visual aspect, the MSDSS model has the abil-

ity to produce a picture representative of data input

rather than abstract. It is able to transfer data from it

to other software, applicable to building material se-

lection, and present the properties of each material in

a successive window.

• User weightings have been included in the selection

methodology to supplement, and not supplant human

judgment in the decision-making process. By incor-

porating user weightings into the selection process,

the methodology gains greater acceptability to the

user who supplies the weightings.

• Materials change in their innovation, composition,

price and availability and most tools find it challeng-

ing to update information relating to products. In this

MSDSS model, the materials and the corresponding

performance of the selected products is updated

through a link to the manufacturers web page on the

internet, and the users may access more information

regarding the selected material or technology through

internet from the supplier’s web pages.

• The system has been designed to produce an artistic

output, accurate, detailed representation and close to

reality as much as it can be, without attempt to con-

ceal any feature whether attractive or not;

• Provision of only a limited set of operations or crite-

ria restricts the techniques and solutions that can be

applied and consequently restricts the decision-mak-

ing process. On the other hand, the inclusion of many

objectives and the permitting of user specification of

input data, system parameters and models, generally

increases system flexibility and increases decision

support freedom;

• In most tools, AHP technique at the pare-wise com-

parison stage, tend to be quite cumbersome and often

takes a lot of time to maintain the consistency of the

response. To eliminate this challenge MSDSS auto-

matically debugs the system at every stage of the

evaluation and selection process.

• The system has been thoroughly debugged to be less

error prone, so that practitioners can integrate the de-

cisions made by the tools more smoothly into practice,

and that it takes less than few seconds to respond to

users inputs;

• Responses/feedback from system programmers and

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

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126

accredited green building experts have also been in-

cluded in the study to prove the ease of use, applica-

bility and usability of the MSDSS model (see appen-

dix A). As a result, some features have been adjusted

based on expert feedbacks to support more reliable

and expedient, timelier feedback to different design

alternatives or changes.

Reflective Summary

This paper discussed the process of developing a deci-

sion-support system to support choices in low-cost green

building materials. The research presented in this paper

acknowledged the lack of a reliable database model that

decision makers can readily use to aid informed deci-

sion-making when selecting low-cost green materials for

low-cost green residential housing development. The

findings from the reviewed literature and the results of

the surveyed questionnaire further underscored the need

for improving understanding of relevant data associated

with the use of such building materials and components,

with the goal to change and positively influence the cur-

rent mental models, attitudes and priorities of multiple

stakeholders involved in the production of the built en-

vironment, so as to encourage their wider-scale use in

mainstream housing.

Based on the data obtained from selected expert

builder/developer companies, a prototype MSDSS model

was developed to aid designers in making informed deci-

sions regarding their choice of materials for low-cost

green residential housing projects. This model was con-

solidated in to an excel-based decision tool that allows

designers to select low-cost green building products from

a range of possibilities, and view the resulting impacts

and difference in the cost, durability and performance of

a range of alternatives. An analysis using the Analytical

Hierarchy Process (AHP), based on the results of the

participants was performed to show how optimal choices

could change with changing user weightings and vari-

ables. The participants gained views from participating in

the evaluation exercise for a real-life project, including

the difficulties in choosing preference scores.

This study thus, indicates that perhaps the develop-

ment of a DSS model associated with the impacts of low-

cost green building materials is useful in that it gives

designers a new approach of going through the process of

value elicitation, which allows them to explicitly and

transparently test the impacts of their elicited values.

Providing a visual representation, allowing designers or

specifiers to compare multiple alternatives across multi-

ple criteria, was a particularly useful aspect of this study.

7. Conclusions

This report has demonstrated how a DSS model can be

used to support multi-stakeholder involvement in the

selection of low-cost green construction materials in

ways that enable building energy performance and life-

cycle cost to be considered at the early stage of residen-

tial housing design. The study further reinforced the sig-

nificance in taking a multi-attribute approach to assessing

a building product’s sustainable performance. To achieve

this goal, the AHP model of decision-making [57-60]

was adopted to deal with the ambiguities involved in the

assessment of material alternatives and relative impor-

tance weightings of multiple factors, given its ability to

solve multi-criteria decision-making (MCDM) between

finite alternatives.

To prove the validity of the model and the feasibility

of the proposed selection methodology, a real-life but

hypothetical application scenario was used to further

illustrate the application of the MSDSS model in select-

ing the most appropriate floor material for a single

5-bedroom residential housing project located in the

Sutton County of London. The results demonstrated the

capabilities of the system, and exposed the way in which

the system transparently demonstrates the implications of

each step of the analysis. It also proved the practicality of

using the MSDSS model, as it combines multiple factors

into a single performance value that is easily interpreted.

Since the purpose of this research study was to de-

velop an innovative concept to demonstrate a step-by-

step methodology for selecting low-cost green materials

with reasonable accuracy and in real time, as opposed to

developing a fully-equipped commercial software,

macro-in-excel database management technique was

used in the back-end of the system to integrate the large

volumes of data obtained from multiple sources. Excel

was adopted as the database management system since it

has the capabilities to perform all necessary calculations

and is common enough that most people are familiar with

it.

The process followed to develop the prototype

MSDSS model in this research demonstrates that, de-

pending on the domain and scope of the problem at hand,

a DSS can be built fairly quickly and can be used effec-

tively to help designers quantify how they compare ma-

terials that are yet to be certified under the standard

specifications and codes of practice, and that which are

already permitted under existing codes.

However further work is required to fully validate the

MSDSS and the methodology presented. To do so, this

research intends to run further case studies ideally using

“live” building design projects, by comparing the outputs

from the algorithms of the MSDSS system to monitored

data from the completed case study building, in order to

review the potential savings of the new materials or

components proposed by the MSDSS model.

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7.1. Contributions to Research and Industry

Insights identified from addressing the research objec-

tives in Section 3 represent part of the original contribu-

tion to knowledge made by this study. The following are

itemised as key contributions of the study to research and

practice:

• The contribution of this research includes the consid-

eration of a holistic approach to low-cost green build-

ing product selection based on socio-cultural, techni-

cal, emotive, site, cost and environmental perform-

ance. Pre-design estimators and pre-construction

managers could improve their estimating and product

selection practices using the proposed MSDSS tool.

• Material suppliers can also benefit from this approach,

as they can use it to enhance their pricing strategies,

marketing plans, and overall product competitiveness.

• Decision problems about a product’s choice are usu-

ally unstructured and ill-defined. By suggesting an

alternative means of integrating the available re-

sources associated with the informed selection of

low-cost green building materials, it is hoped that the

model will help decision makers to further refine their

material selection criteria thus, encourage effective

decision-making.

• The material selection process is characterized by

competitive objectives, involving multiple stake-

holders and key actors, dynamic and uncertain pro-

cedures and limited timeframes to make significant

decisions. The decision makers within this domain:

the designers, specifiers and other stakeholders are

often confronted with conflicting subjective prefer-

ences and fragmented expertise; hence resulting in

decision-making failures. The capacity of the system

to compare materials using multiple factors with

user-specified weightings, will therefore, encourage

decision-makers to explicitly consider the effects of

their previously-implicit judgments on the outcome of

the project, and thus make choices that are timely,

and result in more sustainable residential housing

project design and implementation.

• The ability to quickly quantify and qualify the suit-

ability outcomes of alternative materials may en-

courage greater industry acceptance of innovative

technology for materials that are yet to be certified

under the standard specifications and codes of prac-

tice.

• The overall approach used here could be tested in

other contexts to determine its generalizability and

applicability. In other words, the system could be ex-

tended to select materials for commercial develop-

ment or for any other purpose.

• The material selection factors identified in the proto-

type model of the MSDSS, provides a unique insight

into sustainability and environmental design informa-

tion requirements for low-cost green housing.

• The adopted research methodology (see Table 1)

employed to address the research objectives in Sec-

tion 3 represents part of the original contribution to

knowledge made by this study.

• The number of academic publications on the impacts

of low-cost green materials was found to be low;

hence makes a crucial contribution.

• In the short term, the model could be used in the

housing sector as a catalogue of materials to support

decision-making in low-cost green housing designs.

• As low-cost green building materials and components

become well understood by design and building pro-

fessionals, there is a likelihood of reducing over-de-

pendency on conventional construction materials in

the housing industry.

• The outcome of this study could aid top executives

within the housing sector to consider low-cost green

materials as part of existing regulatory frameworks

and building codes of the Construction Standards In-

stitute (CSI) in capital projects. By doing so, such an

approach may create a potential market for local

manufacturing and processing of such materials.

7.2. Setbacks, Challenges and Probable Solutions

There were few possible limitations that this research

faced during the cause of the study. The limitations are

hereby listed for future consideration.

• The process of developing the selection methodology

was faced with critical issues that led to several

changes in the research methodology and its objec-

tives so many times, in order to achieve the aim of

this research.

• Citing prior research studies formed the basis of the

literature review and helped lay the foundation for

understanding the research problem investigated in

this study. However, there were reservations regard-

ing the currency and scope of the research topic, as

there was no compelling evidence of prior research on

the topic. As literature on DSS for low-cost green

housing design is still relatively low, the study there-

fore had to rely on the most current reports, inter-

views, and observations from the different and vari-

ous organisations, and building professionals for its

information.

• It remains true that sample sizes that are too small

cannot adequately support claims of having achieved

valid conclusions and sample sizes that are too large

do not permit the deep, naturalistic, and inductive

analysis that defines qualitative inquiry [47]. Yin [47]

noted that determining adequate sample size in quali-

tative research is ultimately a matter of judgment and

experience in evaluating the quality. Hair et al. [61]

warned that it is important to consider not only the

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

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128

statistical significance, but also the quality and prac-

tical significance of the results for managerial appli-

cations, when analysing data. They noted that unequal

or uneven sample sizes amongst different professional

groups could also bias or influence the results as get-

ting equal sample sizes from different groups of re-

spondents was unrealistic and demanding. To address

this issue the study adopted a sampling strategy using

the stratified random sampling approach where each

group of the sample population had reasonable num-

ber of randomly selected participants, which helped to

achieve sampling equivalence between the researcher

and professionals of the various building professions

both in higher institutions and practicing building de-

sign and housing construction firms.

• Giving that most respondents were practicing profes-

sionals, getting a list of the sample population for the

study was very discouraging. Having access to people,

and organizations, was otherwise limited, giving the

time differences and tight-scheduled activities. How-

ever the use of progressive approach of reminding the

subjects using any available means either through

e-mails, LinkedIn, Facebook, Twitter or through phone

calls helped to address this problem.

• Very few of the participants had little exposure to

AHP quantitative-based decision-making process.

Though they found the process a bit daunting, they

were somewhat comfortable with the idea of ranking

preferences, as they were used to considering the

choice for alternatives based on unquantified methods,

but without assigning personal values to criteria. Prior

help manual sent to participants before embarking on

expert evaluation survey helped to reduce the com-

plexities associated with the MCDM technique

adopted.

7.3. Potential Areas for Further Studies

Several areas were identified as potential areas for fur-

ther research as itemised below:

• Although not demonstrated in this system but it is

also possible that potential researchers can redesign

or customize the database to best fit the needs of any

particular region or could be extended to select mate-

rials for commercial development;

• While the findings of this research focused specifi-

cally on a subset of design and building professionals

involved with public residential housing sector pro-

jects, the overall approach used here could be tested

in other contexts to determine its generalizability and

applicability.

8. Acknowledgements

This work was made possible through private funding,

and was partially supported by a discount from the Jour-

nal of Building Construction and Planning Research

Doctoral Research Student Discount Scheme program.

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APPENDIX A: Feedbacks from Evaluators

The following are feedbacks and suggestions retrieved

from users on the MSDS tool. The names of the partici-

pants were undisclosed to respect their anonymity.

“The system relates to issues concerned with local

knowledge, local materials data, local climate know-how,

local experts needed to operate system, which are hardly

considered in other systems”. I think it shows great

promise and the mechanics are very well-developed and

user-friendly,

“Material costs vary from location to location (espe-

cially in the USA where material costs vary not just from

state to state but also from city to city”. Perhaps when

the material selection is sorted by the element choice,

this will seem more useful”.

“It depends on what resources you are referring to; if

referring to the underlying database, those are consider-

able. If referring to the resource needs of the organiza-

tion that would use the model, not too costly to operate”.

“The interface is very well-designed and easy to navi-

gate. However, there is a need for more explanatory ma-

terial to allow the user to understand what s/he is actually

doing, and how to operate some parts of the model ap-

propriately”.

“In terms of its operation, interoperability, flexibility,

usability and applicability, per se, it is very clear and

straightforward; it's the underlying premise and data that

needs little clarification in order for the user to operate

the model effectively.