The objective of this work is to incorporate metaheuristic optimization into the framework of the reliability analysis of geostructures in conjunction with innovative tools for treating computational intensive problems of real-world geostructural systems. For the purposes of this study two types of random variables are considered: those which influence demand and those that affect capacity. The Monte Carlo simulation method is considered as the most reliable method for estimating exceedance probabilities or other statistical quantities albeit with excessive, in many cases, computational cost, while first or second order reliability methods (FORM, SORM) constitute alternative approaches. In this study, in order to propose an efficient methodology for performing reliability analysis of geostructures we assess and compare seven metaheuristic optimization algorithms incorporated into FORM for solving reliability analysis problems, while one neural network approximation of the limit-state function is also examined. The results suggest that the seven approaches examined offer applicable as well as fast solutions but with varying qualitative characteristics. 
Reliability analysis of geostructures based on metaheuristic optimization