scholarly journals Reliability Analysis for Retaining Pile in Foundation Pit Based on Bayesian Principle

2022 ◽  
Vol 130 (2) ◽  
pp. 1135-1148
Author(s):  
Yousheng Deng ◽  
Chengpu Peng ◽  
Jialin Su ◽  
Lingtao Li ◽  
Liqing Meng ◽  
...  
2011 ◽  
Vol 147 ◽  
pp. 197-202 ◽  
Author(s):  
Jiang Zhou ◽  
Jing Cao ◽  
Yu He ◽  
Jie Song

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.


2013 ◽  
Vol 671-674 ◽  
pp. 240-244
Author(s):  
Chang Ning Sun ◽  
Jing Cao ◽  
Hai Ming Liu ◽  
Hui Min Zhao

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.


2009 ◽  
Author(s):  
Ronald Laurids Boring ◽  
Johanna Oxstrand ◽  
Michael Hildebrandt

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