Based on an example of a project in Tangshan, the high-rise buildings are built in karst area and mined out affected area which is trea t ed by high pressure grouting, and foundation is adopted the form of pile raft foundation. By long-term measured settlement of high-rise buildings, It is found that foundation settlement is linear increase with the increase of load before the building is roof-sealed, and the settlement increases slowly after the building is roof-sealed, and the curve tends to converge, and the foundation consolidation is completed. The settlement of the foundation is about 80% - 84% of the total settlement before the building is roof-sealed. Three layer BP neural network model is used to predict the settlement in the karst area and mined affected area. Compared with the measured data, the relative difference of the prediction is 0.91% - 2.08% in the karst area, and is 0.95% - 2.11% in mined affected area. The prediction results of high precision can meet the engineering requirements.
In recent years, the construction area of high-rise buildings has become more and more complex, such as: Karst area, mined out area, mined out affected area, subsidence area, tidal flat area, filling area, etc. The complexity of geological conditions will lead to instability of the ground in the field area, the security of the building is seriously threatened, coupled with the increase of the height of the building, the load also increases, and the foundation is prone to uneven settlement. In order to ensure the safety of the building, the use of modern information equipment to monitor the settlement of the foundation and the collected data analyzed for future settlement prediction are important parts of engineering construction indispensable under complicated geological conditions [
There are many methods for predicting the settlement of foundation, which can be reduced to two kinds. One is a purely theoretical method of calculation, which is more difficult to predict when calculating the relevant parameters. The other is the prediction method based on measured data, the method to establish the model with the measured data, the prediction results of higher accuracy, such as regression analysis, time series method, hyperbola method, grey theory method, artificial neural network method [
The construction in complex geological conditions of the high-rise buildings, because the underground situation is unknown, leading to many parameters difficult to define, coupled with its perplexing changes, and it is difficult to give a mathematical formula for each stage of the construction, but due to the influence of human factors, in various stages of settlement monitoring will have errors, these with the increase of time continuous superposition error. If the prediction model cannot eliminate the human error and the system error in the observation result, the result will be very different from the actual situation. For the prediction of settlement data of high-rise building foundation under complicated geological conditions, BP neural network has its unique advantages, the traditional function of the independent variables can be used as the inputs of the model, and the traditional function of the dependent variable can be used as the output of the model, and this function is transformed into multi dimensional nonlinear mapping, through the network like the brain the information processing process of parallel processing, will not affect the results of the whole small part because the loss of data. The network contact between the contact behavior between the simulated brain neurons, adaptive, self-organizing and self- learning characteristics, error information can eliminate the settlement monitoring results in the continuous learning of the correct information simulation, through the simulation to predict the future settlement [
A neural network consists of a large number of neurons interconnected by certain rules. The interaction between neurons connection to play the role of information processing, distributed information and knowledge reserves by neurons, automatic change of weight in neural network is the core part of the whole network, through the network automatically changing the weights, until it reaches the desired output. Its basic characteristics are: nonlinear function’s extreme approximation, fault tolerance, learning adaptability and parallel processing [
Rumelhart and McCelland [
Based on the Tangshan A plaza including A tower in karst area and E4 high- rise residential building in the mined out affected area. Combined with their long-term measured settlement data, analysis of building foundation settlement rule, and establish the BP neural network prediction model, to predict the foundation settlement.
Three layer feed-forward BP neural network prediction model is adopted, and the learning steps of the network are as follows: [
Middle layer node operation formula:
Output layer node operation formula:
where, f is a nonlinear function; q is the neuron threshold;
The transfer function, also known as the stimulus function, is the stimulus intensity between the feedback layer and the layer node. In the (0, 1) continuous value, the Sigmoid function is one of the most commonly used transfer functions. The formula is as follows:
The difference between the output of the output layer and the expectation is the error of the network. The formula is as follows:
where,
Through error back propagation, the network adjusts the weights and thresholds of each layer automatically, and the error continues to decrease until the infinity tends to 0. The process is called self learning process:
where,
A square in Tangshan is located in Lunan District of Tangshan City. The area includes karst development, goaf wave and seismic fracture zone. According to the engineering characteristics and physical and mechanical properties of the soil layer, the soil layer in the field area can be divided into 14 layers from top to bottom (as shown in
The A tower is in karst area, underground karst develops. The main building is 23 storey frame shear wall structure, the underground two layers are underground garage and the frame structure. From April 2010 to July 2011, during the period, a total of 18 monitoring were conducted, the accumulation settlement data of eight settlement observation points of the high building was obtained. E4 building is adjacent to the former site of Fengnan coal mine. It is affected by the influence of mined out area. E4 high-rise residential buildings is a 34 storey
Layer number | Name of rock soil | Unit weigh (kN/m3) | Void ratio e | Water content (%) | Shear strength | Modulus of compressibility Es (MPa) | Characteristic value of subgrade bearing capacity fak (kPa) | |
---|---|---|---|---|---|---|---|---|
internal friction angle | Cohesion C(kPa) | |||||||
2 | Silty clay | 18.0 | 0.615 | 27 | 23.0 | 25.0 | 5.2 | 140 |
3 | Silty sand | 19.0 | 0.594 | 22 | *21.0 | *4.5 | 9.0 | 160 |
4 | Fine sand | 20.0 | 0.593 | 23 | *24.0 | *5.5 | 9.5 | 190 |
5 | Silty clay | 20.5 | 0.598 | 25 | 13.0 | 8.0 | 5.3 | 160 |
6 | Fine sand | 19.5 | 0.589 | 20 | *28.0 | *5.5 | 13 | 230 |
7 | Silty clay | 20.5 | 0.604 | 26 | 23.0 | 43.9 | 5.5 | 160 |
8 | Fine sand | 20.1 | 0.596 | 22 | *27.0 | *5.5 | 14.5 | 250 |
9 | Silty clay | 20.6 | 0.574 | 24 | 23.2 | 48.2 | 5.7 | 170 |
10 | Residual soil | 18.0 | 0.654 | 25 | *23.5 | *13.5 | 7.0 | 260 |
shear wall structure, and the underground two layer is an underground garage. From March 2010 to May 2011, during the period, a total of 30 monitoring were conducted, the accumulation settlement data of eight settlement observation points of the high building was obtained. The P-v-t curves are shown in
The following conclusions can be drawn from
1) A tower cumulative settlement observation point the highest point for the point 1, the cumulative settlement is 15.50 mm, the cumulative settlement minimum point for settlement observation point 6, the cumulative settlement is 14.31 mm. High-rise residential buildings cumulative settlement observation point the highest point for the point 27, the cumulative settlement is 26.0 mm, the cumulative settlement minimum point for settlement observation point 32, the cumulative settlement is 22.5 mm. All conform to the standard [
2) A tower and E4 high-rise residential building, the monitoring points are basically the same trend, no more than the standard allowed uneven settlement occurred. The pile raft foundation has good applicability in the mined out area of the karst area after grouting treatment. It can basically eliminate the adverse effects of the mined out area on the ground.
3) The total settlement of the A tower at each settlement observation point is 81% - 83% of the total settlement, and the accumulated settlement accounts for 17% - 19% of the total settlement after the cap is completed. Before the settlement of the E4 high-rise residential building, the accumulated settlement accounts for 82% - 83% of the total settlement, and the accumulated settlement accounts for 18% - 19% of the total settlement. Most of the settlement of the foundation occurred before loading.
4) From the P-s-t curve of A tower and E4 high-rise residential buildings can be seen in stories with the increase of load settlement is increasing linearly, constant load after the cap, the settlement curve increased slowly convergent, complete consolidation.
5) Tilt of the building body are calculated, respectively A and E4 tower high-rise building settlement difference between the two observation points of maximum calculated inclination values, calculated A tower tilt value is 0.0015, E4 value of 0.0018 high-rise residential building tilt, all meet the requirements of the standard [
The maximum cumulative sedimentation rate of point 16 is chosen as the settlement prediction. The cumulative sedimentation value of the original observations is shown in
For the measured settlement data of karst area foundation, selects 18 issues of settlement observation data in the first stage 8 data as the analysis sample, predict 10 period after sedimentation value, the predicted results with 10 phase of the measured data, after discussing the model accuracy. The predictions are shown in
The 30 issue of settlement observation data is selected in the first 20 period as the training sample data, to predict the settlement of 10 period, the 10 period after prediction results and the measured data, the prediction results are shown in
nper | date | Number of days (d) | Cumulative Settlement (mm) | nper | date | Number of days (d) | Cumulative Settlement (mm) |
---|---|---|---|---|---|---|---|
1 | 2010/04/12 | 0 | 0 | 10 | 2010/08/11 | 121 | 8.80 |
2 | 2010/04/25 | 13 | 0.47 | 11 | 2010/08/21 | 131 | 9.74 |
3 | 2010/05/28 | 26 | 1.00 | 12 | 2010/08/31 | 141 | 10.45 |
4 | 2010/05/16 | 34 | 1.48 | 13 | 2010/09/10 | 151 | 11.44 |
5 | 2010/06/06 | 55 | 2.97 | 14 | 2010/09/20 | 161 | 12.49 |
6 | 2010/06/24 | 73 | 4.35 | 15 | 2010/09/30 | 171 | 13.66 |
7 | 2010/07/09 | 88 | 5.43 | 16 | 2010/10/30 | 201 | 14.16 |
8 | 2010/07/20 | 99 | 6.86 | 17 | 2011/01/30 | 293 | 14.46 |
9 | 2010/07/31 | 110 | 7.95 | 18 | 2011/07/30 | 476 | 14.66 |
nper | date | Number of days (d) | Cumulative Settlement (mm) | nper | date | Number of days (d) | Cumulative Settlement (mm) |
---|---|---|---|---|---|---|---|
1 | 2010/03/21 | 0 | 0 | 16 | 2010/05/31 | 72 | 9.98 |
2 | 2010/04/01 | 11 | 0.56 | 17 | 2010/06/04 | 76 | 10.54 |
3 | 2010/04/08 | 18 | 1.43 | 18 | 2010/06/08 | 80 | 11.18 |
4 | 2010/04/13 | 23 | 2.10 | 19 | 2010/06/12 | 84 | 11.68 |
5 | 2010/04/17 | 28 | 2.68 | 20 | 2010/06/16 | 88 | 12.27 |
6 | 2010/04/21 | 32 | 3.35 | 21 | 2010/06/20 | 92 | 12.90 |
7 | 2010/04/25 | 36 | 4.11 | 22 | 2010/06/24 | 96 | 13.60 |
8 | 2010/04/29 | 40 | 4.83 | 23 | 2010/06/29 | 101 | 14.37 |
9 | 2010/05/03 | 44 | 5.46 | 24 | 2010/07/04 | 106 | 15.18 |
10 | 2010/05/07 | 48 | 6.08 | 25 | 2010/07/09 | 111 | 15.93 |
11 | 2010/05/11 | 52 | 6.79 | 26 | 2010/07/13 | 115 | 6.52 |
12 | 2010/05/15 | 56 | 7.54 | 27 | 2010/07/18 | 120 | 17.19 |
13 | 2010/05/19 | 60 | 8.08 | 28 | 2010/08/18 | 150 | 17.98 |
14 | 2010/05/23 | 64 | 8.82 | 29 | 2010/11/19 | 240 | 18.67 |
15 | 2010/05/28 | 69 | 9.49 | 30 | 2011/05/20 | 420 | 19.25 |
nper | Cumulative settlement (mm) | The BP neural network | Residual (mm) | Relative difference (%) |
---|---|---|---|---|
9 | 7.95 | 8.0377 | 0.0877 | 1.10 |
10 | 8.80 | 8.8932 | 0.0932 | 1.06 |
11 | 9.47 | 9.5748 | 0.1048 | 1.11 |
12 | 10.45 | 10.5532 | 0.1037 | 0.97 |
13 | 11.44 | 11.5436 | 0.1036 | 0.91 |
14 | 12.49 | 12.6477 | 0.1577 | 1.26 |
15 | 13.66 | 13.8313 | 0.1713 | 1.25 |
16 | 14.16 | 14.4136 | 0.2536 | 1.79 |
17 | 14.46 | 14.7343 | 0.2743 | 1.90 |
18 | 14.66 | 14.9417 | 0.2817 | 1.92 |
nper | Cumulative settlement (mm) | The BP neural network | Residual (mm) | Relative difference (%) |
---|---|---|---|---|
21 | 12.90 | 13.0225 | 0.1225 | 0.95 |
22 | 13.60 | 14.0057 | 0.1357 | 1.00 |
23 | 14.37 | 14.5275 | 0.1578 | 1.10 |
24 | 15.18 | 15.3337 | 0.1537 | 1.01 |
25 | 15.93 | 16.0895 | 0.1595 | 1.00 |
26 | 16.52 | 16.7037 | 0.1837 | 1.11 |
27 | 17.19 | 17.4025 | 0.2125 | 1.24 |
28 | 17.98 | 18.2718 | 0.2918 | 1.62 |
29 | 18.67 | 19.0087 | 0.3387 | 1.81 |
30 | 19.25 | 19.6125 | 0.3625 | 1.88 |
1) A tower and E4 high-rise residential building, the monitoring points are basically the same trend, no more than the standard allowed uneven settlement occurred. The pile raft foundation has good applicability in the mined out area of the karst area after grouting treatment. It can basically eliminate the adverse effects of the mined out area on the ground.
2) The cumulative sedimentation of a tower settlement is 81% to 83% of the total settlement. The cumulative settlement volume of E4 high-rise residential buildings is 82% to 83% of the total settlement. Most ground subsidence occurs during loading in front of the cap.
3) Three layer feed forward BP neural network prediction models are adopted to predict foundation settlement prediction in the karst area and mined out affected area in high-rise buildings. The predicting results are close to the measured values and the difference between 1% and 2%. So the predicting model can be applied in high-rise building foundation settlement analysis and prediction.
4) The BP neural network prediction model will be more advantageous in the short-term settlement prediction, as the model of the growth model becomes less accurate over time.
Ding, J.H., Li, B.J., Du, E.X., Wang, W.Y. and Zhao, T. (2017) Analysis and Prediction of Foundation Set- tlement of High-Rise Buildings under Com- plex Geological Conditions. World Journal of Engineering and Technology, 5, 445-454. https://doi.org/10.4236/wjet.2017.53039