Reliability analysis of an embankment dam slope based on an ellipsoid model and PSO-ELM

Abstract

Accurately comprehending the intricate characteristics of geotechnical parameters can be challenging due to the complexity of geotechnical engineering and the lack of available information. The accuracy and efficiency of conventional reliability analysis methodologies are impeded by the copiousness of raw data. This study proposed a non-probabilistic method for reliability analysis, which employed an ellipsoidal model integrated with a machine learning algorithm. This novel method characterized parameter uncertainties by their boundaries, utilized the synergy between particle swarm optimization and extreme learning machine for performance function surrogate, and employed an ellipsoidal model to calculate the reliability index. The proposed method was applied to evaluate the stability of an embankment dam slope in France. The results revealed the significant influence of the alterations in internal friction and cohesion on the stability of the embankment dam slope. Therefore, it can be imperative to account for the variability and correlation of soil parameters when conducting a rigorous analysis of embankment dam slope stability. Notably, the proposed method can circumvent the requirement for employing probability distribution forms to represent parameters, while concurrently ensuring a commendable level of computational efficiency and accuracy. Furthermore, the proposed method can be compatible with probabilistic analysis methods in evaluating the stability of the embankment dam, thereby providing an effective method for conducting reliability analyses of such structures.