Probabilistic risk assessment of earth dams with spatially variable soil properties using random adaptive finite element limit analysis

authored by
Kang Liao, Yiping Wu, Fasheng Miao, Yutao Pan, Michael Beer
Abstract

Risk assessment of earth dams is concerned not only with the probability of failure but also with the corresponding consequence, which can be more difficult to quantify when the spatial variability of soil properties is involved. This study presents a risk assessment for an earth dam in spatially variable soils using the random adaptive finite element limit analysis. The random field theory, adaptive finite element limit analysis, and Monte Carlo simulation are employed to implement the entire process. Among these methods, the random field theory is first introduced to describe the soil spatial variability. Then the adaptive finite element limit analysis is adopted to obtain the bound solution and consequence. Finally, the failure probability and risk assessment are counted via the Monte Carlo simulation. In contrary to the deterministic analysis that only a factor of safety is given, the stochastic analysis considering the spatial variability can provide statistical characteristics of the stability and assess the risk of the earth dam failure comprehensively, which can be further used for guiding decision-making and mitigation. Besides, the effects of the correlation structure of strength parameters on the stochastic response and risk assessment of the earth dam are investigated through parametric analysis.

Organisation(s)
Institute for Risk and Reliability
External Organisation(s)
China University of Geosciences
Norwegian University of Science and Technology (NTNU)
Type
Article
Journal
Engineering with computers
Volume
39
Pages
3313-3326
No. of pages
14
ISSN
0177-0667
Publication date
10.2023
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Software, Modelling and Simulation, Engineering(all), Computer Science Applications
Electronic version(s)
https://doi.org/10.1007/s00366-022-01752-0 (Access: Closed)