Reliability Analysis of Foundation Pit Based on Response Surface of Support Vector Machine

Traditional analysis methods of reliability in the foundation pit engineering have larger error and larger amount of calculation. Therefore, the response surface method has attracted much attention because it can effectively use the finite element analysis method (FEAM) and reduce the number of the numerical simulation. This paper combines uniform design (UD) with support vector machine (SVM). On this base, a reliability analysis method of the foundation pit is put forward based on the response surface of support vector machine (RSSVM). The UD structures random samples and the FEAM is used to obtain corresponding response parameters including the lateral displacement of wall, settlement of ground, safety factor of overall stability and safety factor of against overturning. Then, SVM trains the above random samples and corresponding response parameters to get response surface (RS) respectively. The probability density distribution of each response parameter is obtained by combining the Monte Carlo method with RSSVM. The instance analysis shows that the method has high computing efficiency and less amount of calculation, and the result is reasonable. It provides an effective way for the reliability analysis of the foundation pit engineering.

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Application of SVM to Reliability Analysis of Foundation Excavations System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.859.315 ◽

2013 ◽

Vol 859 ◽

pp. 315-321 ◽

Cited By ~ 1

Author(s):

Jing Cao ◽

Chang Ning Sun ◽

Hai Ming Liu

Keyword(s):

Support Vector Machine ◽

Reliability Analysis ◽

Response Surface ◽

Correlation Coefficient ◽

Failure Modes ◽

Pearson Correlation ◽

Support Vector ◽

Random Parameters ◽

Pearson Correlation Analysis ◽

Instance Analysis

The correlation of failure modes needs to be considered in the reliability analysis of foundation excavations system. Because it is difficult to calculate the correlation coefficient of failure modes, the computational efficiency of traditional method is low. In this paper, the response surface (RS) is established by using the uniform test and support vector machine (SVM). On this basis, in order to obtain the index of each failure mode, the random parameters generated by Monte Carlo simulation are predicted. Combined with the Pearson correlation analysis, the correlation coefficient of failure modes is obtained. And then, the Breadth Border Method, Narrow Bounds Method and PNET method are used to calculate system failure probability of foundation excavations. The reliability analysis method of the foundation excavations system based on the response surface of the support vector machine (RSSVM) is put forward. The instance analysis shows that the method is simple in calculation, and provides a convenient way for the system reliability theory of foundation excavations.

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Reliability Analysis of Foundation Pit by Improved Response Surface Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.147.197 ◽

2011 ◽

Vol 147 ◽

pp. 197-202 ◽

Cited By ~ 2

Author(s):

Jiang Zhou ◽

Jing Cao ◽

Yu He ◽

Jie Song

Keyword(s):

Reliability Analysis ◽

Response Surface ◽

Structural Reliability ◽

Lateral Displacement ◽

Limit State ◽

Uniform Design ◽

Design Parameters ◽

State Function ◽

Foundation Pit ◽

Improved Response Surface Method

Lacking of explicit limit state function (LSF) will result large quantities of computational efforts for a FEAM based structural reliability analysis. An improved response surface (RS) method is proposed to analyze the failure probability of foundation pit through combining uniform design (UD) and non-parametric regression (NPR). Deferent levels of design parameters are first delicately selected according to UD and then FEAM is used to analysis corresponding pit response parameters including maximum lateral displacement of wall, settlement of ground, safety factor of overall stability, safety factors of against overturning, heave and piping. The RS relationship is then established through NPR based on inputs and responses. At last, a direct Mont Carlo Simulation is carried out to obtain the probability density function of response parameters.

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Reliability analysis of foundation pit engineering using support vector machine

10.1109/isttca53489.2021.9654795 ◽

2021 ◽

Author(s):

Jichao Xie ◽

Peng Liu

Keyword(s):

Support Vector Machine ◽

Reliability Analysis ◽

Support Vector ◽

Foundation Pit

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A dynamic reliability analysis method based on support vector machine and Monte Carlo simulation

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-193473 ◽

2020 ◽

Vol 20 (1) ◽

pp. 149-155

Author(s):

Xiuqiang Pan ◽

Yangu Zhang ◽

Yi Wan

Keyword(s):

Support Vector Machine ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Reliability Analysis ◽

Support Vector ◽

Analysis Method ◽

Dynamic Reliability

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Vibration reliability analysis for aeroengine compressor blade based on support vector machine response surface method

Journal of Central South University ◽

10.1007/s11771-015-2687-3 ◽

2015 ◽

Vol 22 (5) ◽

pp. 1685-1694 ◽

Cited By ~ 3

Author(s):

Hai-feng Gao ◽

Guang-chen Bai

Keyword(s):

Support Vector Machine ◽

Reliability Analysis ◽

Response Surface ◽

Response Surface Method ◽

Compressor Blade ◽

Support Vector ◽

Surface Method ◽

Vibration Reliability

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Application of BP Neural Network to Risk Analysis of Deep Foundation Pit

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.5989 ◽

2014 ◽

Vol 556-562 ◽

pp. 5989-5993

Author(s):

Lu De Zou ◽

Dong Wei Cao

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Lateral Displacement ◽

Uniform Design ◽

Probability Density Distribution ◽

Soil Parameters ◽

Deep Foundation ◽

Foundation Pit ◽

Deep Foundation Pit ◽

Random Samples

there are many uncertainty factors in the design process of the deep foundation pit engineering, such as the soil parameters, loading, which make the calculated displacement, settlement and safety factor have randomness and uncertainty. This paper combines uniform design (UD) with BP neural network. The UD structures random samples. Then, BP neural network trains random samples and the corresponding lateral displacement, settlement of ground and safety factors to get response relationship respectively. On this basis, the probability density distribution of each response parameter is obtained by predicting a large number of samples obtained by the Monte Carlo simulation. And then the Breadth Border Method, Narrow Bounds Method and PNET method are used to calculate system failure probability of foundation pit. The instance analysis shows that the method has high computing efficiency and the result is reasonable. It provides an effective way for the reliability analysis of the foundation pit engineering.

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