Copula-based reliability and sensitivity analysis of aging dams: Adaptive Kriging and polynomial chaos Kriging methods

Time-dependent reliability and sensitivity analysis, in which the nature of demand, capacity and the limit state function varies over the life cycle of the structural system, is a challenging task. Meta-models are a suitable tool to construct inexpensive-to-evaluate and accurate surrogates in analysis of modern engineering problems. The paper's contribution is threefold: First, the superiority of polynomial chaos Kriging (PCK) meta-model in terms of efficiency and accuracy is depicted in comparison to two other state-of-the-art methods in an explicit representation of a pilot gravity dam. Second, the problem of aging dams is analytically studied. Adaptive reliability approaches which benefit from Kriging and PCK meta-models are also investigated in a comparative analysis with classical reliability methods. Third, the importance of considering nonlinear dependency between random variables by copula theory is investigated under the reliability and sensitivity concepts. The results show that PCK meta-model can be used as an effective technique in uncertainty quantification (UQ) of dams. Furthermore, Kriging and PCK-assisted reliability methods can establish fairly accurate meta-models to perform reliability analysis in structural UQ with low computational efforts. Finally, the concept of UQ is propagated to dam class in which the dam shape and its age are assumed to be variable. A generalized program is developed to assist dam owners and decision-makers in approximate failure probability estimation of gravity dams. This paper paves the road for global risk assessment of dams. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper discusses time-dependent reliability and sensitivity analysis, comparing the superior efficiency and accuracy of the polynomial chaos Kriging meta-model with two other state-of-the-art methods. It examines aging dam issues, adaptive reliability approaches, and the significance of considering nonlinear dependency between random variables in reliability and sensitivity concepts.Overall, the paper highlights the potential of using PCK meta-model in uncertainty quantification and establishing accurate meta-models for reliability analysis in structural UQ.