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

Abstract

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 proposes 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 determine 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 generation 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.

Keywords

System reliability / Machine learning method / Non-dominated sorting genetic algorithm / Robust design / Multiple objective optimization models

CRediT authorship contribution statement

Li Hong: Conceptualization, Methodology, Formal analysis, Writing – Original Draft. Xiangyu Wang: Software, Supervision, Funding acquisition. Wengang Zhang: Software, Writing – Reviewing and Editing, Supervision, Funding acquisition. Yongqin Li: Data Curation, Writing – Reviewing and Editing. Runhong Zhang: Visualization, Funding acquisition. Chunxia Chen: Funding acquisition, database collection.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors are grateful to the financial support from National Natural Science Foundation of China (No. 52078086), Postdoctoral innovative talents support program, Chongqing (Grant No. CQBX2021022), Financial support from China Southwest Geotechnical Investigation & Design Institute Co., Ltd (C2021-0264).

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