Three-dimensional slope reliability and risk assessment using auxiliary random finite element method

Xiao, Te, Li, Dian-Qing, Cao, Zi-Jun, Au, Siu-Kui ORCID: 0000-0002-0228-1796 and Phoon, Kok-Kwang (2016) Three-dimensional slope reliability and risk assessment using auxiliary random finite element method. COMPUTERS AND GEOTECHNICS, 79. pp. 146-158.

Text 160507-ARFEM for 3D Slope - Accept.pdf - Author Accepted Manuscript Download (1MB)

Abstract

This paper aims to propose an auxiliary random finite element method (ARFEM) for efficient three-dimensional (3-D) slope reliability analysis and risk assessment considering spatial variability of soil properties. The ARFEM mainly consists of two steps: (1) preliminary analysis using a relatively coarse finite-element model and Subset Simulation, and (2) target analysis using a detailed finite-element model and response conditioning method. The 3-D spatial variability of soil properties is explicitly modeled using the expansion optimal linear estimation approach. A 3-D soil slope example is presented to demonstrate the validity of ARFEM. Finally, a sensitivity study is carried out to explore the effect of horizontal spatial variability. The results indicate that the proposed ARFEM not only provides reasonably accurate estimates of slope failure probability and risk, but also significantly reduces the computational effort at small probability levels. 3-D slope probabilistic analysis (including both 3-D slope stability analysis and 3-D spatial variability modeling) can reflect slope failure mechanism more realistically in terms of the shape, location and length of slip surface. Horizontal spatial variability can significantly influence the failure mode, reliability and risk of 3-D slopes, especially for long slopes with relatively strong horizontal spatial variability. These effects can be properly incorporated into 3-D slope reliability analysis and risk assessment using ARFEM.

Item Type:	Article
Uncontrolled Keywords:	Slope stability, Reliability analysis, Risk assessment, Spatial variability, Random finite element method, Response conditioning method
Depositing User:	Symplectic Admin
Date Deposited:	05 Oct 2016 08:18
Last Modified:	19 Jan 2023 07:29
DOI:	10.1016/j.compgeo.2016.05.024
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3003605