A new and low cost proposed method for failure probability estimation based on Monte Carlo simulation

In recent years, evaluation of reliability analysis of structures has become a straightforward and interesting topic among structural researchers. In the field of civil engineering, estimation of structural failure probability is highly investigated, considering probabilistic uncertainties of resistance and load parameters during modeling and designing of structures. To obtain an accurate failure probability, researchers often perform Monte Carlo simulation (MCS) that requires numerous modeling and cost. In the present study, based on MCS, an enhanced technique is developed that efficiently reduces number of structural modeling. The approach works by employing the concept of First Order Reliability Methods (FORMs) to determine closest point to the mean of random variables in failure region. Afterward, by presenting sub-intervals in various layers, to group generated samples in MCS, the possibility of reducing number of structural modeling with well-disciplined error and acceptable accuracy is achieved. In order to investigate the efficiency and robustness of the method, various numerical and engineering examples with complex limit state functions were attempted and the obtained results were compared with those based on the conventional reliability methods in the literature. Results show the high accuracy of proposed approach while the number of requires structural modeling were substantially reduced.