Zhongguo dizhi zaihai yu fangzhi xuebao (Feb 2024)

Probabilistic inverse-analysis and reliability prediction of rainfall-induced landslides for slope with multi-source information

  • Honghu JIE,
  • Shuihua JIANG,
  • Zhilu CHANG,
  • Jinsong HUANG,
  • Faming HUANG

DOI
https://doi.org/10.16031/j.cnki.issn.1003-8035.202309029
Journal volume & issue
Vol. 35, no. 1
pp. 28 – 36

Abstract

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Probabilistic inverse-analysis is an essential approach to infer statistical characteristics of uncertain soil parameters, making the slope reliability assessment closer to engineering reality. However, current probabilistic inverse analysis rarely integrates multi-source information, including monitored data, field observation information, and slope survival records. Conducting the probabilistic inverse-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating the multi-source information is a challenging issue due to the involvement of thousands of random variables and the evaluation of high-dimensional likelihood functions. In this paper, a modified Bayesian updating with subset simulation (mBUS) method is combined with adaptive conditional sampling (aCS) algorithm to establish a framework for probabilistic inverse analysis of spatially variable soil parameters and reliability prediction of slopes. The effectiveness of this framework is validated using a highway slope as a case study. The research results show that the posterior statistical characteristics of soil parameters obtained by integrating multi-source information are in good agreement with field observation results. Additionally, the probability of slope failure under heavy rainfall on September 12, 2004 with the updated soil parameters is 23.1 %, which is in line with the actual slope instability. Within this framework, multi-source information can be fully utilized to address high-dimensional probabilistic inverse analysis problems.

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