Efficient System Reliability Analysis for Layered Soil Slopes with Multiple Failure Modes Using Sequential Compounding Method

Liao, Kang, Wu, Yiping, Miao, Fasheng, Zhang, Longfei and Beer, Michael ORCID: 0000-0002-0611-0345 (2023) Efficient System Reliability Analysis for Layered Soil Slopes with Multiple Failure Modes Using Sequential Compounding Method. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 9 (2). 04023015-.

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Official URL: http://dx.doi.org/10.1061/ajrua6.rueng-1022

Abstract

Evaluating the system reliability of layered soil slopes is a challenging issue because multiple failure modes may be included along the slip surfaces, which makes the overall failure probability greater than any individual slip surface. In this paper, an efficient system reliability analysis concerning the layered soil slopes is conducted based on the sequential compounding method (SCM) that has the ability to compound multiple failure events into an equivalent event sequentially. First, the first order reliability method (FORM) is employed to quantify initial reliability indices and correlation coefficients among these failure modes. Subsequently, the SCM is used to calculate the equivalent reliability indices and correlation coefficients until the multiple failure events are reduced to a compound event, and then the system reliability of the slope is obtained accordingly. The application of the approach to probabilistic evaluation of layered slopes is illustrated by two typical examples, and the correctness is verified by a Monte Carlo simulation (MCS). The results show that the SCM can deliver accurate system failure probability and greatly improve the computational efficiency compared with the MCS, which is an advantageous and promising strategy in evaluating the system reliability of layered soil slopes.

Item Type:	Article
Uncontrolled Keywords:	System reliability, Layered soil slopes, Multiple failure modes, First order reliability method (FORM), Sequential compounding method (SCM), Monte Carlo simulation (MCS)
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	24 Apr 2023 07:32
Last Modified:	15 Mar 2024 05:26
DOI:	10.1061/ajrua6.rueng-1022
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169892