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

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.

Automated summary

This study presents a risk assessment for earth dams in spatially variable soils using the random adaptive finite element limit analysis. It introduces the random field theory to describe soil spatial variability, adopts the adaptive finite element limit analysis to obtain the bound solution and consequence, and counts the failure probability and risk assessment through Monte Carlo simulation. The stochastic analysis considering spatial variability can provide statistical characteristics of stability and comprehensively assess the risk of earth dam failure, which is useful for decision-making and mitigation.