Probabilistic back analysis of rainfall-induced slope failure considering slope survival records from past rainfall events

Xin Liu, Yu Wang*, Anthony Kwan Leung

*Corresponding author for this work

Research output: Contribution to journalJournal Articlepeer-review

9 Citations (Scopus)

Abstract

Many rainfall-induced landslides occur on slopes that have limited, or even no, site investigation data before failure. Probabilistic back analysis of slope failure provides an effective tool to back analyze possible pre-failure soil parameters, thus gaining insights into the mechanism of slope failure. For a slope failure induced by rainfall, the actual factor of safety (FS) is time-variant when time-variant rainfall and infiltration are explicitly modeled. This study proposes a novel probabilistic back analysis method that models explicitly the rainfall triggering mechanism for a rainfall-induced slope failure. Unlike existing methods that are based on a constant FS = 1, the proposed method utilizes FS inequality information for probabilistic back analysis, including slope failure record with FS < 1 and slope survival records from past rainfall events with FS > 1. The proposed method is suitable for high-dimensional problems when soil hydraulic properties are also back analyzed. The proposed method converges to the conventional methods with FS = 1 when the time span of slope failure is narrowed down to a few hours and slope survival records are ignored. Incorporating both slope failure and survival records effectively reduces uncertainties in soil strength and hydraulic parameters.

Original languageEnglish
Article number105436
JournalComputers and Geotechnics
Volume159
DOIs
Publication statusPublished - Jul 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Back analysis
  • Probabilistic methods
  • Rainfall-induced landslide
  • Slope stability

Fingerprint

Dive into the research topics of 'Probabilistic back analysis of rainfall-induced slope failure considering slope survival records from past rainfall events'. Together they form a unique fingerprint.

Cite this