首页|System reliability-based robust design of deep foundation pit considering multiple failure modes

System reliability-based robust design of deep foundation pit considering multiple failure modes

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Recently,reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical engineering.However,for deep foundation pit,evaluating the system safety of retaining structures and finding cost-effective design points are main challenges.To address this,this study pro-poses a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou,China.The proposed method included two parts:system reliability model and robust design method.Back Propagation Neural Network(BPNN)is used to fit limit state functions and conduct efficient reliability analysis.The common source random variable(CSRV)model are used to evaluate correlation between failure modes and deter-mine the system reliability.Furthermore,based on the system reliability model,a robust design method is developed.This method aims to find cost-effective design points.To solve this problem,the third gen-eration non-dominated genetic algorithm(NSGA-Ⅲ)is adopted.The efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-Ⅲ algorithm.The proposed method has a good performance in locating the balanced design point between safety and construction cost.Moreover,the proposed method can provide design points with reasonable stiffness distribution.

System reliabilityMachine learning methodNon-dominated sorting genetic algorithmRobust designMultiple objective optimization models

Li Hong、Xiangyu Wang、Wengang Zhang、Yongqin Li、Runhong Zhang、Chunxia Chen

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School of Civil Engineering,Chongqing University,Chongqing 400045,China

School of Design and the Built Environment,Curtin University,Bentley 6102,Western Australia,Australia

Key Laboratory of New Technology for Construction of Cities in Mountain Area,Chongqing University,Chongqing 400045,China

Institute of Smart City and Intelligent Transportation,Southwest Jiaotong University,Chengdu 610097,China

China Southwest Geotechnical Investigation & Design Institute Co.,Ltd,Chengdu 610052,Sichuan,China

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国家自然科学基金Postdoctoral innovative talents support program,ChongqingChina Southwest Geotechnical Investigation & Design Institute Co.,Ltd

52078086CQBX2021022C2021-0264

2024

10.1016/j.gsf.2023.101761

地学前缘(英文版)
中国地质大学（北京） 北京大学

地学前缘(英文版)

CSTPCD
影响因子：0.576
ISSN：1674-9871
年,卷(期)：2024.15(2)
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