Engineering, 2010, 2, 408-419
doi:10.4236/eng.2010.26054 Published Online June 2010 (
Copyright © 2010 SciRes. ENG
Experimental Investigation and Development of Artificial
Neural Network Model for the Properties of Locally
Produced Light Weight Aggregate Concrete
Mostafa A. M. Abdeen1, Hossam Hodhod2
1Department of Engineering, Mathematics & Physics, Faculty of Engineering, Cairo University, Giza, Egypt
2Department of Structural Engineering, Faculty of Engineering, Cairo University, Giza, Egypt
E-mail: {mostafa_a_m_abdeen, hossamhodhod}
Received December 27, 2009; revised February 21, 2010; accepted February 26, 2010
The developments in the field of construction raise the need for concrete with less weight. This is beneficial
for different applications starting from the less load applied to foundations and soil till the reduction of car-
nage capacity required for lifting precast units. In this paper, the production of light weight concrete from
light local weight aggregate is investigated. Three candidate materials are used: crushed fired brick, vermicu-
lite and light exfoliated clay aggregate (LECA). The first is available as the by-product of brick industry and
the later two types are produced locally for different applications. Nine concrete mixes were made with same
proportions and different aggregate materials. Physical and mechanical properties were measured for con-
crete in fresh and hardened states. Among these measured ones are unit weight, slump, compressive and ten-
sile strength, and impact resistance. Also, the performance under elevated temperature was measured. Re-
sults show that reduction of unit weight up to 45%, of traditional concrete, can be achieved with 50% reduc-
tion in compressive strength. This makes it possible to get structural light weight concrete with compressive
strength of 130 kg/cm2. Light weight concrete proved also to be more impact and fire resistant. However, as
expected, it needs separate calibration curves for non-destructive evaluation. Following this experimental
effort, the Artificial Neural Network (ANN) technique was applied for simulating and predicting the physical
and mechanical properties of light weight aggregate concrete in fresh and hardened states. The current paper
introduced the (ANN) technique to investigate the effect of light local weight aggregate on the performance
of the produced light weight concrete. The results of this study showed that the ANN method with less effort
was very efficiently capable of simulating the effect of different aggregate materials on the performance of
light weight concrete.
Keywords: Light Weight Concrete, Locally Produced Aggregate, Ultrasonic Pulse Velocity, Modeling, Artificial
Neural Network
1. Introduction
One of the main disadvantages of using concrete for
construction is its high weight. Recent applications of
high rise buildings, long span buildings, and precast
elements require reduction in weight to keep concrete a
competent construction material. Several approaches for
reducing concrete weight were introduced as in Rama-
chandran et al. [1], ACI [2], ACI [3], ACI [4] and
Neville [5]. These include production of aerated concrete,
light weight aggregate concrete, and cellular concrete.
The first type has quite a low strength which makes it
unsuitable for structural applications. Cellular concrete is
produced from traditional concrete materials with apply-
ing advanced technology to produce concrete with align-
ed voids that do not reduce its structural capacity for a
specific application (usually for slabs; where reduction in
weight is most appreciated). The application of light wei-
ght aggregate is the approach that reduces weight (since
aggregate occupies about 70% of concrete volume) and
maintains the merit of using traditional techniques of
production. The feasibility of applying some material to
construction results from its local availability. Therefore,
investigation of feasibility of local aggregate for produc-
tion of light weight concrete is of great importance.
Since light weight aggregate has the merit of heat insula-
tion, search in locally applied aggregate for insulation
purposes would shorten the search for suitable light
weight aggregate. In Egypt, LECA and crushed fired
clay brick (a by-product of brick industry) are used for
heat insulation. Therefore, they appear as good candi-
dates for the required investigation. Vermiculite is lo-
cally produced fro insulation purpose too. However, its
small size suggests its application as a replacement of
fine aggregate. It remains to investigate the structural
properties of concrete made from these aggregates since
they are not usually applied for structural purposes.
It is quite clear from the literature mentioned previ-
ously the amount of experimental effort required to ac-
curately investigate and understand the properties of light
weight concrete. This fact urged the need for utilizing
new technology and techniques to facilitate this compre-
hensive effort and at the same time preserving high ac-
Artificial intelligence has proven its capability in
simulating and predicting the behavior of the different
physical phenomena in most of the engineering fields.
Artificial Neural Network (ANN) is one of the artificial
intelligence techniques that have been incorporated in
various scientific disciplines. Ramanitharan and Li [6]
utilized ANN with back-propagation algorithm for mod-
eling ocean curves that were presented by wave height
and period. Tawfik, Ibrahim and Fahmy [7] showed the
applicability of using the ANN technique for modeling
rating curves with hysteresis sensitive criterion. Abdeen
[8] developed neural network model for predicting flow
depths and average flow velocities along the channel
reach when the geometrical properties of the channel
cross sections were measured or vice versa. Allam [9]
used the artificial intelligence technique to predict the
effect of tunnel construction on nearby buildings which
is the main factor in choosing the tunnel route. Allam, in
her thesis, predicted the maximum and minimum differ-
ential settlement necessary precautionary measures. Az-
mathullah et al. [10] presented a study for estimating the
scour characteristics downstream of a ski-jump bucket
using Neural Networks (NN). Abdeen [11] presented a
study for the development of ANN models to simulate
flow behavior in open channel infested by submerged
aquatic weeds. Mohamed [12] proposed an artificial neu-
ral network for the selection of optimal lateral load-re-
sisting system for multi-story steel frames. Mohamed, in
her master thesis, proposed the neural network to reduce
the computing time consumed in the design iterations.
Abdeen [13] utilized ANN technique for the develop-
ment of various models to simulate the impacts of dif-
ferent submerged weeds’ densities, different flow dis-
charges, and different distributaries operation scheduling
on the water surface profile in an experimental main
open channel that supplies water to different distributar-
2. Problem Description
To study the effect of light local weight aggregate as
well as elevated temperature on the performance of the
produced light weight concrete (compressive, tensile, im-
pact, stiffness, and rebound number, ultrasonic pulse
velocity), experimental and numerical techniques will be
presented in this study. The experimental program and its
results will be described in detail in the following sec-
tions. The numerical models presented in this study util-
ized Artificial Neural Network technique (ANN) using
the data of the experiments and then can predict the per-
formance of concrete for different mix proportions.
3. Experimental Program
Ten mixes were planned to be cast. In the first one, Tra-
ditional constituents were used (cement, sand, gravel and
water). In the rest, coarse aggregate was replaced by one
or more light weight aggregates. In these nine mixes, fine
aggregate was partially or totally replaced by vermiculite
(as a light weight fine aggregate). The slump was meas-
ured for all mixes in the fresh state. Hardened concrete
was tested to measure 7 & 28 day compressive strength,
splitting tensile strength, Modulus of elasticity, Impact
resistance, Rebound number, and ultrasonic pulse veloc-
ity. Some specimens were subjected to elevated tempera-
ture of 40℃ for one hour and tested to get residual com-
pressive strength, Rebound number, and ultrasonic pulse
4. Materials and Specimens
Constituents for concrete mixes were: water, cement,
sand, gravel, vermiculite, crushed fired clay brick, and
LECA. Their main properties are shown as follows.
4.1. Water
Tap water was used for mixing and curing of all speci-
4.2. Cement
The used cement is Ordinary Portland cement CEM I -
42.5N complying with ES4756 and EN-197. It is pro-
duced locally in EGYPT by Helwan Cement Company.
4.3. Sand
Siliceous sand with fineness modulus of 3.04 was used. It
Copyright © 2010 SciRes. ENG
Copyright © 2010 SciRes. ENG
4.8. Fibers
has a specific gravity of 2.6 and bulk density of 1.7 kg/L.
4.4. Gravel Natural Linen fibers were used in three mixes to evaluate
the possible enhancement of properties when fibers are
applied. These fibers are widely used in Egypt for deco-
rative elements made from white cement and gypsum
pastes. They have average diameter of 0.8 mm and
comes in spools. They were cut to a length of 40 mm to
be suitable for mixing with concrete constituents.
Desert gravel from Dahshour quarry was used as in
Figure 1. It has maximum nominal aggregate size of
25 mm. It has a specific gravity of 2.5 and bulk density
of 1.56 kg/L.
Mix proportions for all ten mixes are shown in Table
1. The replacement of fine and coarse aggregates (sand
and gravel) was made by bulk volume. Mechanical tilt-
ing type mixer (140 L capacity) was used where dry ma-
terials were mixed first for one minute. Then water was
added and mixing continued, for almost two minutes, till
a homogeneous mixture was obtained. Some hand mix-
ing was made at the end to resolve the problem of float-
ing LECA.
4.5. LECA
Light Exfoliated Clay Aggregate (LECA) which is pro-
duced locally in Egypt by National Cement Company
was used as a replacement of gravel. LECA particles are
almost round with rough texture (Figure 1). They have
maximum nominal size of 25 mm and contain a ratio
ranging from 0.4-0.5 (by weight) with particle size in
the range 4.76-2.4 mm. LECA has a bulk density of From each mix Specimens in forms of cubes (150 mm),
beams (100 × 100 × 500 mm) and cylinders (150 ×
0.58 kg/L.
300 mm) were cast in a steel forms. Specimens were
covered with plastic sheets in lab for 24 hours and were,
then, demolded. Specimens were cured by immersion in
tap water at 22C till day of testing or 28 days whichever
is less.
4.6. Crushed Fire Clay Brick
Crushed fired clay brick is a by-product from brick in-
dustry. It has irregular shape but can be crushed to have
maximum nominal size of 25 mm as in Figure 1. The
measured bulk density of the crushed brick used in this
study is 1.2 kg/L.
5. Test Results
The test results are explained in details in the following
sections. The results of the numerical models and the test
ones are drawn together in a same figure for each test to
show the power of the models.
4.7. Vermiculite
Vermiculite produced by Egyptian Vermiculite Company
was used to replace sand. It is usually applied as heat
insulator and have a light weight (SG = 0.55). Particles
of vermiculite are almost round as shown in Figure 1
and have size in the range of 1-5 mm.
5.1. Slump
The Slump was measured for all mixes after unloading of
mixer. Test results are shown in Table 2. One can see
that crushed bricks have the worst effect on slump. This
can be attributed to its high absorption (10.5%). Also,
combination of LECA and vermiculite seems to have an
adverse effect on slump.
5.2. Bulk Density
LECA Gravel Bulk density of different concrete mixes was measured
on cube specimens in the hardened state after 28 days.
Results are shown in Table 2. One can see that the
maximum reduction in weight could be achieved using
mix No. 5. This implies that LECA is most efficient in
reducing weight of concrete.
5.3. Compressive Strength
Crushed Bricks Vermiculite Compressive strength was measured at two ages: 7 and
28 days. Results are shown in Table 2 for all mixes.
Figure 1. Different types of aggregate.
M. A. M. ABDEEN ET AL. 411
Table 1. Mix proportions for all studied concrete mixes.
Concrete Constituent- kg/m3 (*)
Mix No Water Cement Sand Vermiculite Gravel LECA Crushed
1 200 400 560 ---- 1120 ---- ---- ----
2 200 400 50 50 ---- 100 ---- ----
3 200 400
30 70 ---- 100 ---- ----
4 200 400
--- 100 ---- 100 ---- ----
5 200 400
30 ---- ---- 100+70 ---- ----
LECA replaced
both fine and
coarse aggregates.
6 200 400
--- 100 ---- 50 50 ----
7 200 400
100 ---- ---- ---- 100 ----
8 200 400
--- 100 ---- 100 ---- 0.4
9 200 400
30 70 ---- 100 ---- 0.4
10 200 400
30 ---- ---- 100+70 ---- 0.4
LECA replaced
both fine and
coarse aggregates.
(*) Only for Mix (1) and water, fibers, and cement content for all mixes. Replacement of aggregate is made by bulk volume and shown values
(in the shaded cells are percentage of replacement from bulk volume used in Mix No. 1.
(**) This replacement was made since LECA has 0.4-0.5 of its weight with particle size in the range 2.4-4.76 mm.
Table 2. Physical and mechanical properties of different concrete mixes.
Strength - kg/cm2
Impact Resistance
Density (kg/L)
7-d 28-d
N1 N2(*)
1 200 2.41 200 268 25.5 191.6 216 ------
2 30 1.74 61 71 14.9 33.7 986 ------
3 68 1.46 100 122 17.0 54.1 350 ------
4 100 1.34 116 130 18.1 32.4 506 ------
5 50 1.34 102 118 14.9 54.6 640 ------
6 15 1.90 108 122 22.9 56.2 259 ------
7 40 2.25 211 238 19.1 98.3 549 ------
8 35 1.55 63 95 12.7 52.7 543 903
9 45 1.51 66 102 16.0 150.5 653 854
10 25 1.44 93 104 22.3 126.4 857 1020
(*) Applicable only when fibers exist.
One can see that the most light concrete mixes (4, 5)
gives almost 50% of compressive strength of traditional
concrete (Mix No. 1). The relationship between strength
and density is known to be an inverse proportion. The
plot of this relationship for the current study is shown in
Figure 2.
5.4. Tensile Strength
Splitting tensile strength tests were made on standard
cylinders as shown in Figure 3. Results are shown in
Table 2 for all mixes. One can see, from Table 2, the
positive Effect of fibers on tensile strength. One can also
see from Figure 4 that correlation between tensile and
compressive strength differ for normal weight (Mixes 1
& 7) and light weight concretes (rest of mixes). Tensile
strength represents higher percentage of compressive
strength for light weight concrete.
28dCompressiveStrength‐ kg/cm2
E xperimental
Figure 2. Compressive strength vs. specific gravity for stud-
ied concrete mixes.
opyright © 2010 SciRes. ENG
Copyright © 2010 SciRes. ENG
Figure 3. Specimens loaded in splitting tension test.
0.010.0 20.0 30.0 40.0 50.0
S plittingTensileStrength‐kg/cm2
E xperiment
Figure 4. Compressive strength vs. splitting tensile strength.
5.5. Impact Strength
Impact tests were made on concrete discs (150 × 100 mm)
according to ACI [14]. Results of number of blows till
first crack (N1) and number of blows till separation (N2)
are shown in Table 2. It shall be mentioned that N2 can
be measured only for fibrous concrete. It can be seen that
light weight concrete (LWC) absorbs more energy than
normal weight concrete. Part of this is attributed to the
high deformability of LWC where the falling hammer
caused a significant deformation in surface (Figure 5)
which resulted in distribution of energy on larger area.
This was not the case for normal weight concrete. Also,
one can see the positive effect of adding fibers on impact
5.6. Stiffness (Modulus of Elasticity)
The stiffness of concrete was measured via measuring se-
cant modulus of elasticity (E) on standard cylinders ac-
cording to ASTM [15]. Results are shown in Table 2 for
all mixes. It can be seen that adding of fibers increases
Figure 5. Impact tests on concrete from different mixes (top:
mix-1, bot.: Mix-4).
stiffness of LWC significantly. Also, the low value of E
for LWC confirms the high deformability observed in
impact test. Values of modulus of elasticity are plotted vs.
Compressive strength in Figure 6. The dashed line re-
presents the equation given in Egyptian code of practice
(ECP 203/2007)). The ECP equation represents an upper
bound to all data. However, deviation is quite large for
LWC values. This implies that another relationship shall
be used for concluding E from compressive strength in
case of LWC.
5.7. Non Destructive Evaluation of Concrete
A common evaluation of concrete structure is made via
two methods: rebound hammer measurements and ultra-
sonic pulse velocity measurements as in Malhotra [16],
ACI [17] and ACI [18]. Usually, calibration curves are
constructed to correlate the previous measurements with
concrete compressive strength. The studied mixes were
tested at 7 and 28 days to get the non-destructive meas-
urements on standard specimens. Then specimens were
loaded in compression to get their actual compressive
0.0E +005.0E +041.0E +051.5E +052.0E +052.5E +05
SecantModulusofEl a s ti c i ty ‐kg/cm2
Figure 6. Compressive strength vs. modulus of elasticity.
Copyright © 2010 SciRes. ENG
strength. Correlations between measurements and com-
pressive strength were then plotted to show consistency
in behavior between different concrete mixes.
300 30.040.0 50.0
E xperimental
5.7.1. Rebound Hammer
Readings of rebound hammer as an average of 10 read-
ings taken on two opposite faces of concrete cubes are
shown in Table 3. Measurements were made at ages of 7
and 28 days. One can see that, generally, there is a slight
difference in readings on same concrete at the two ages.
Figure 7 shows the correlation between rebound
number (RN) and compressive strength at 28 days. One
can see the clear distinction between values for tradi-
tional mixes (1, 7) having normal weight, and other
LWC mixes. LWC show lower rebound values. Fiber
inclusion did not make any enhancement to rebound
Figure 7. Correlation between rebound number (RN) and
compressive strength at 28 days.
cubes and, second, indirect measurements were made at
the surface of concrete beams.
5.7.2. Ultrasonic Pulse Velocity
As shown in Figure 8 ultrasonic pulses velocity were
measured using Pundit apparatus. Two speeds were
measured. First, direct measurement was made through
One can see a slight increase in indirect pulse velocity
over the direct one. Figure 9 shows the correlations be-
tween pulse velocity (V1) in direct transmission and (V2)
in indirect transmission versus concrete compressive
strength. Although transmission speed in normal weight
concrete is higher than LWC, it seems to follow same
correlation with compressive strength as LWC. This is in
direct conformance with the accepted fact that both com-
pressive strength and sonic speed are directly proportional
to density.
Table 3. Non-destructive evaluation of Concrete from dif-
ferent mixes.
Direct trans-
US Pulse Ve-
V1 (km/sec)
Indirect trans-
US Pulse Veloc-
V2 (km/sec)
7-d 28-d 7-d 28-d 7-d 28-d
1 33.5 35.7 4.38 4.59 4.29 4.75
2 15.9 15.0 2.48 2.68 2.82 2.85
3 16.0 22.6 3.03 3.07 3.39 3.09
4 19.0 24.5 2.60 2.88 2.83 3.34
5 23.2 25.1 2.97 3.21 3.36 3.30
6 25.8 31.8 3.08 3.12 3.37 4.00
7 36.3 36.0 3.68 3.61 3.97 4.47
8 21.0 22.6 2.62 2.96 2.52 3.55
9 22.5 24.3 2.62 2.98 2.97 3.62
10 23.5 22.4 2.79 3.04 3.13 3.55
5.7.3. Effect of Elevated Temperature
One of the known advantages of using LWC is their re-
sistance to elevated temperature. Standard concrete cubes
and beams from all tested mixes were exposed to 400C
for one hour. Cubes were evaluated via the above men-
tioned techniques. Then cubes were tested in compres-
sion to get the residual compressive strength. Values for
the three evaluation methods are shown in Table 4. One
can see that residual strength is higher than 80% for all
LWC mixes. For normal weight concrete mixes, the
Table 4. Destructive and non-destructive evaluation of concrete from different mixes exposed to 400C for one hour.
(before Expo-
of residual
Direct trans-
US Pulse
V1 (km/sec))
Indirect trans-
US Pulse
V2 (km/sec)
1 268 122 46 27.7 0.73 1.50
2 82 66 80 24.3 2.61 2.51
3 118 116 98 26.6 2.71 3.31
4 107 86 80 27.5 2.34 3.00
5 86 73 85 27.2 2.82 3.00
6 129 154 119 29.3 2.74 3.20
7 238 127 53 31.7 2.80 3.80
8 95 75 79 27.7 2.60 3.00
9 102 88 86 28.4 2.64 3.00
10 104 93 89 28.0 2.70 2.51
Figure 8. Direct and indirect measurements of ultrasonic
pulse velocity in concrete.
0.0 1.0 2.03.0 4.05.0
28-d v1- km/sec
E xperimental
28-d v2- km/sec
28-d Comp Strength - kg/cm2
E xperimental
Figure 9. Correlation between ultrasonic pulse velocity and
compressive strength.
value is almost 50%. This confirms the expected high re-
sistance of LWC to elevated temperature. It shall be men-
tioned that exposure to elevated temperature was made at
ages higher than 28 days and the strength was measured in
compression test at the same day of exposure. Values of
compressive strength measured before exposure is shown
in the second column in Table 4 below.
Readings of rebound hammer and ultrasonic pulse ve-
locity, in direct and indirect transmission, are shown in
Table 4 too. Graphical Presentation of non-destructive
data is made in Figures 10 and 11 below. One can see
that rebound values decreased significantly for normal
weight concrete although some increase in RN was ob-
served for LWC. This, again, confirms the resistance of
LWC to elevated temperature. It can be seen from Fig-
ure 11 that same behavior was observed for pulse trans-
mission speed. The effect of temperature on speed was
300 30.040.0 50.0
E xperimental
Figure 10. Correlation between rebound number (rn) and
compressive strength after one hour exposure to 400C.
400 C v1- km/sec
400CompStrength‐ kg/cm2
400 C v2- km/sec
400CCompStrength‐kg/cm 2
E xperimental
Figure 11. Correlation between ultrasonic pulse velocity and
compressive strength after one hour exposure to 400C.
opyright © 2010 SciRes. ENG
higher for indirect transmission since it reflects the in-
tegrity of concrete surface that is most affected by expo-
6. Numerical Model Structure
Neural networks are models of biological neural struc-
tures. Abdeen [8] described in a very detailed fashion the
structure of any neural network. Briefly, the starting
point for most networks is a model neuron as shown in
Figure 12. This neuron is connected to multiple inputs
and produces a single output. Each input is modified by a
weighting value (w). The neuron will combine these
weighted inputs with reference to a threshold value and
an activation function, will determine its output. This
behavior follows closely the real neurons work of the
human’s brain. In the network structure, the input layer is
considered a distributor of the signals from the external
world while hidden layers are considered to be feature
detectors of such signals. On the other hand, the output
layer is considered as a collector of the features detected
and the producer of the response.
6.1. Neural Network Operation
It is quite important for the reader to understand how the
neural network operates to simulate different physical
problems. The output of each neuron is a function of its
inputs (Xi). In more details, the output (Yj) of the jth neu-
ron in any layer is described by two sets of equations as
For every neuron, j, in a layer, each of the i inputs, Xi,
to that layer is multiplied by a previously established
weight, wij. These are all summed together, resulting in
the internal value of this operation, Uj. This value is then
Figure 12. Typical picture of a model neuron that exists in
every neural network.
biased by a previously established threshold value, tj, and
sent through an activation function, Fth. This activation
function can take several forms such as Step, Linear,
Sigmoid, Hyperbolic, and Gaussian functions. The Hy-
perbolic function, used in this study, is shaped exactly as
the Sigmoid one with the same mathematical representa-
tion, as in Equation (3), but it ranges from –1 to +1 rather
than from 0 to 1 as in the Sigmoid one (Figure 13)
fx e (3)
The resulting output, Yj, is an input to the next layer or
it is a response of the neural network if it is the last layer.
In applying the Neural Network technique, in this study,
Neuralyst Software, Shin [19], was used.
6.2. Neural Network Training
The next step in neural network procedure is the training
operation. The main purpose of this operation is to tune
up the network to what it should produce as a response.
From the difference between the desired response and
the actual response, the error is determined and a portion
of it is back propagated through the network. At each
neuron in the network, the error is used to adjust the
weights and the threshold value of this neuron. Conse-
quently, the error in the network will be less for the same
inputs at the next iteration. This corrective procedure is
applied continuously and repetitively for each set of in-
puts and corresponding set of outputs. This procedure
will decrease the individual or total error in the responses
to reach a desired tolerance.
Once the network reduces the total error to the satis-
factory limit, the training process may stop. The error
propagation in the network starts at the output layer with
the following equations:
ijijj i
wwLReX (4)
 
jj jjj
eY YdY
where, wij is the corrected weight, w
ij is the previous
Figure 13. The sigmoid activation function.
Copyright © 2010 SciRes. ENG
Copyright © 2010 SciRes. ENG
weight value, LR is the learning rate, ej is the error term,
Xi is the ith input value, Yj is the ouput, and dj is the de-
sired output.
7. Simulation Cases
To fully investigate numerically the physical and me-
chanical properties of concrete with different aggregate
materials (crushed fired brick, vermiculite and LECA),
several simulation cases are considered in this study.
These simulation cases can be divided into two groups.
The first group simulates the effect of aggregate type on
the performance of concrete: density, compressive, ten-
sile, modulus of elasticity, rebound hammer reading and
ultrasonic pulse velocities. The second group simulates
the effect of elevated temperature on the performance of
concrete with different aggregate materials: compressive
strength, rebound hammer reading and ultra sonic pulse
7.1. Neural Network Design
To develop a neural network model to simulate the effect
of aggregate type on the performance of concrete, first
input and output variables have to be determined. Input
variables are chosen according to the nature of the prob-
lem and the type of data that would be collected in the
field. To clearly specify the key input variables for each
neural network simulation groups and their associated
outputs, Table 5 is designed to summarize all neural
network key input variables and output for the first simu-
lation group, while Tables 6(a) and 6(b) are designed to
summarize the key input and output variables for the se-
cond simulation group respectively.
It can be noticed from Table 5 that the first simulation
group consists of seven simulation cases (seven neural
network models) to study the effect of aggregate type on
the density, compressive strength, tensile strength, mo-
dulus of elasticity, rebound hammer reading and ultra-
sonic pulse velocities. Tables 6(a) and 6(b), for the sec-
ond simulation group, consists of one neural network
model, multi-input and multi-output model, for the case
of elevated temperature to 400 to study the effect of
aggregate type on the compressive strength, rebound ha-
mmer reading and ultrasonic pulse velocities.
Several neural network architectures are designed and
tested for all simulation cases investigated in this study
to finally determine the best network models to simulate,
very accurately, the effect of aggregate type as well as
elevated temperature on the performance of concrete
based on minimizing the Root Mean Square Error (RMS-
Error). Figure 14 shows a schematic diagram for a ge-
neric neural network. The training procedure for the de-
veloped ANN models, in the current study, uses the data
of 9 mixes (1, 2, 4, 5, 6, 7, 8, 9, 10) out of 10 available
mixes, and the mix data number 3 is used to test the
power of prediction of the neural network models. Here,
it is very important to mention that the available data are
very limited (one group of results for every mix) and the
experimental results are changing significantly with the
change of aggregate type.
Table 7 shows the final neural network models for the
two simulation groups and their associate number of
neurons. The input and output layers represent the key
input and output variables described previously for each
simulation case.
The parameters of the various network models devel-
oped in the current study for the different simulation
cases are presented in Table 8, where: (Comp. St.) de-
notes for compressive strength, (Ten. St.) for tensile
using secant modulus of elasticity and (Rn) for rebound
number. These parameters can be described with their
Table 5. Key input variables and output for the first neural network simulation group.
Case Input Variables Output
Density Density
Direct Trans-
mission US
US Velocity
Water Cement SandGravelVermiculiteLECA Bricks Fibers
Table 6. (a) Key input variables for the second neural network simulation group; (b) Key output variables for the second
neural network simulation group.
Simulation Case Input Variables
Effect of Elevated Tem-
perature to 400oC Water Cement Sand Gravel Vermiculite LECA Bricks Fibers
Simulation Case Output Variables
Effect of Elevated Tem-
perature to 400oC
Compressive Strength
Kg/cm2 Rebound Number (Rn) Ultrasonic Velocity - V1
Ultrasonic Velocity – V2
Input # 1
Input # 2
Hidden layer
3 neurons
Hidden layer
3 neurons
Output # 1
Output # 2
Figure 14. General schematic diagram of a simple generic neural network.
Table 7. The developed neural network models for all the simulation cases.
No. of Neurons in each Layer
Simulation Group No. of
Layers Input
First Group 6 8 6 5 4 3 1
Second Group
(Elevated Tem-
6 8 6 5 4 3 4
tasks as follows:
Learning Rate (LR): determines the magnitude of
the correction term applied to adjust each neuron’s
weights during training process = 1 in the current study.
Momentum (M): determines the “life time” of a cor-
rection term as the training process takes place = 0.9 in
the current study.
Training Tolerance (TRT): defines the percentage
error allowed in comparing the neural network output to
the target value to be scored as “Right” during the
training process.
Testing Tolerance (TST): it is similar to Training
Tolerance, but it is applied to the neural network out-
puts and the target values only for the test data.
Input Noise (IN): provides a slight random variation
to each input value for every training epoch = 0 in the
current study.
Function Gain (FG): allows a change in the scaling
or width of the selected function = 1 in the current
Scaling Margin (SM): adds additional headroom, as
a percentage of range, to the rescaling computations
used by Neuralyst Software, Shin (1994), in preparing
data for the neural network or interpreting data from the
neural network = 0.1 in the current study.
Raining Epochs: number of trails to achieve the pre-
sent accuracy.
Percentage Relative Error (PRR): percentage rela-
tive error between the numerical results and actual
measured value for and is computed according to Equa-
tion (6) as follows:
(Absolute Value (ANN_PR – AMV)/AMV) × 100 (6)
Copyright © 2010 SciRes. ENG
Table 8. Parameters used in the developed neural network models.
Simulation Case
Comp. St. Ten. St. Stiff. (E) Rn V1 V2 Elevated
TRT 0.001 0.0001 0.0001 0.001 0.0001 0.0001 0.0001 0.01
TST 0.003 0.0003 0.0003 0.003 0.0003 0.0003 0.0003 0.03
Training Epochs 4667 9165 8094 29872 15688 13739 9512 579123
MPRE 0.15 0.03 0.02 0.6 0.02 0.02 0.01 5.0
RMS-Error 0.0006 0.0 0.0001 0.0004 0.0001 0.0001 0.0 0.012
ANN_PR: Predicted results using the developed ANN
AMV: Actual measured value.
MPRE: Maximum percentage relative error during the
model results for the training step.
8. Results and Discussions
Numerical results using ANN technique are plotted with
the experimental results for the first neural network si-
mulation group (density, tensile strength, modulus of ela-
sticity, rebound hammer reading and ultrasonic pulse ve-
locities) versus 28-days compressive strength as shown
in Figures 2, 4, 6, 7 and 9, respectively. It can be noticed
from these figures that ANN technique can accurately
simulate the effect of aggregate type on the performance
of concrete.
To study the effect of elevated temperature as well as
aggregate type on the performance of concrete (com-
pressive strength, rebound hammer reading and ultra-
sonic pulse velocities), numerically, the second neural
network simulation group are prepared as shown in Ta-
bles 6(a) and 6(b). The results of this group are plotted
with the experimental results in Figures 10 and 11 for
400. One can see from these figures (which, also, are
drawn between the property and 28-days compressive
strength) that ANN technique can accurately simulate the
effect of elevated temperature and aggregate type on the
performance of concrete.
To check the accuracy of neural network the term PRE
is calculated as in Equation (6) for each data point in
each model. Then the Max PRE is calculated through
each model and reported in Table 8. It is very clear from
the row of Max PRE that this value doesn’t exceed 0.6%
for the simulation cases in the first group and for the
second group (elevated temperature) this value reaches
5% because this group is multi input-multi output model
and the available data is very limited.
To check the power of the developed models in the
prediction technique, the training step is made using the
experimental results of 9 mixes out of the 10 available
mixes and date of mix No. 3 is kept for the prediction
comparison. It is very clear from the Figures 2, 4, 6, 7, 9,
10 and 11 and the value of MPRE that the models are
very efficient in simulating the effect of aggregate type
as well as elevated temperature on the performance of
concrete for the 9 mixes of the training step and for the
prediction one the numerical results are quite difference
from the experimental results specially for compressive
strength. That difference in the compressive strength can
be understood for two reasons: first, because of the lim-
ited number of data (one result group for each mix) as it
is mentioned before in a previous section and second, the
compressive strength is highly affected by changing the
type of aggregate so it needs a lot of data to be simulated.
On the other hand, the numerical results for density, ten-
sile strength, rebound number and ultrasonic pulse ve-
locities are very close to the experimental one which
confirms the power of the current developed ANN model
in the prediction technique.
9. Conclusions
Through the course of this study, ten concrete mixes of
normal and light weight concrete were evaluated. LWC
was produced using local processed and recycled aggre-
gates in Egypt. Results showed the potential of the local
aggregate to achieve weight reduction of about 40%.
However, a corresponding reduction in strength was in-
evitable. Similar reduction in stiffness was observed too.
However, it could be compensated for by applying fiber
reinforcement. The application of LWC appeared to be
promising in increasing impact and elevated temperature.
The results of implementing the ANN technique in
this study showed that this approach was capable of iden-
tifying relationship between different uncertain parame-
ters with multiple input/output criterions. The ANN pre-
sented in this study was very successful in simulating
and predicting the effect of aggregate type and elevated
temperature on the performance of concrete.
10. Acknowledgements
The Authors would like to express their gratitude to-
wards Prof. Dr. Farouk El-Hakim of 15th May institute
for Civil and Arch. Engineering, and undergraduate stu-
dents (4th year– civil) for the help they provided during
the experimental part of this research.
1. References
[1] V. J. Ramachandran, et al., “Concrete Science,” Heyden
& Sons Ltd., London, 1981.
opyright © 2010 SciRes. ENG
[2] ACI 211-2-91, “Standard Practice for Selecting Propor-
tions of Structural Light Weight Concrete,” American
Concrete Institute, Michigan, 1991.
[3] ACI 330-91, “Specifications for Lightweight Aggregate
for Structural Concrete,” American Concrete Institute,
Michigan, 1991.
[4] ACI 213-03, “Guide for Structural Light Weight Con-
crete,” American Concrete Institute, Michigan, 2003.
[5] A. M. Neville, “Properties of Concrete,” John Wiley &
Sons Ltd., London, 1997.
[6] K. Ramanitharan and C. Li, “Forecasting Ocean Waves
Using Neural Networks,” Proceeding of the Second
International Conference on Hydroinformatics, Zurich,
[7] M. Tawfik, A. Ibrahim and H. Fahmy, “Hysteresis Sensi-
tive Neural Network for Modeling Rating Curves,” Jour-
nal of Computing in Civil Engineering, ASCE, Vol. 11,
No. 3, 1997, pp. 184-189.
[8] M. A. M. Abdeen, “Neural Network Model for Predicting
Flow Characteristics in Irregular Open Channel,” Scien-
tific Journal, Faculty of Engineering-Alexandria Univer-
sity, Alexandria, Vol. 40, No. 4, 2001, pp. 539-546.
[9] B. S. M. Allam, “Artificial Intelligence Based Predictions
of Precautionary Measures for Building Adjacent to Tun-
nel Rout during Tunneling Process,” Ph. D. Dissertation,
Faculty of Engineering, Cairo University, Giza, 2005.
[10] H. M. Azmathullah, M. C. Deo and P. B. Deolalikar,
“Neural Networks for Estimation of Scour Downstream
of a Ski-Jump Bucket,” Journal of Hydrologic Engineer-
ing, ASCE, Vol. 131, No. 10, 2005, pp. 898-908.
[11] M. A. M. Abdeen, “Development of Artificial Neural
Network Model for Simulating the Flow Behavior in
Open Channel Infested by Submerged Aquatic Weeds,”
Journal of Mechanical Science and Technology, KSME
International Journal, Soul, Vol. 20, No. 10, 2006, pp.
[12] M. A. M. Mohamed, “Selection of Optimum Lateral
Load-Resisting System Using Artificial Neural Net-
works,” M. Sc. Thesis, Faculty of Engineering, Cairo
University, Giza, 2006.
[13] M. A. M. Abdeen, “Predicting the Impact of Vegetations
in Open Channels with Different Distributaries’ Opera-
tions on Water Surface Profile using Artificial Neural
Networks,” Journal of Mechanical Science and Technol-
ogy, KSME International Journal, Soul, Vol. 22, No. 9,
2008, pp. 1830-1842.
[14] ACI 544.2R-99, “Measurement of Properties of Fiber
Reinforced Concrete,” American Concrete Institute, Mi-
chigan, 1999.
[15] ASTM C0469-02E01, “Test Method for Static Modulus
of Elasticity and Poisson’s Ratio of Concrete in Com-
pression,” 2001.
[16] V. M. Malhotra, “Testing Hardened Concrete: Nonde-
structive Methods,” ACI Monograph No. 9, American
Concrete Institute, Michigan, 1986.
[17] ACI 228.1 R89, “In-Place Methods for Determination of
Strength of Concrete,” American Concrete Institute, Mi-
chigan, 1989.
[18] ACI 437 R91, “Strength Evaluation of Existing Concrete
Buildings,” American Concrete Institute, Michigan, 1991.
[19] Y. Shin, “NeuralystTM User’s Guide, Neural Network
Technology for Microsoft Excel,” Cheshire Engineering
Corporation Publisher, California, 1994.
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