System‐level reliability sensitivity analysis by using weighted average simulation method

Abstract

In addition to reliability analysis, investigation of the uncertainties' effect on the safety of structures can be regarded as one of the great concerns in engineering fields. The present study provides an efficient perturbation-based reliability sensitivity analysis approach based on weighted average simulation method (WASM) to attain uncertainties effects on the structures safety. Without asking additional samplings and/or requiring to function derivation (that is necessary in score function method), the proposed approach simultaneously uses the finite difference and weight flexibility feature of WASM to estimate the parameter sensitivities of a reliability problem. The proposed method has also been successfully applied to the improved version of WASM to obtain reliability based sensitivity results with very few samples. The accuracy and efficiency of the proposed method is examined by solving five analytical and engineering examples with highly nonlinear performance functions and system-level reliability problems. For each example, results are compared with those obtained by Monte Carlo simulation and common reliability methods. It is shown that the method is capable of solving real world system-level engineering problems efficiently and accurately.

Exact reliability evaluation of infrastructure networks using graph theory

“…These methods are often used for the reliability assessment of large systems because unlike the non‐simulation‐based methods, the size of reliability problem is essentially independent of the number of system components. Various classifications of simulation‐based methods have been proposed in the literature as follows: Cross‐Entropy Methods; Discrete Event Simulation; Fuzzy Bayesian System Reliability; Line Sampling; Monte Carlo Simulation Method (]); Subset Simulation; Weighted Average Simulation …”

“…Various classifications of simulation-based methods have been proposed in the literature as follows: Cross-Entropy Methods 17 ; Discrete Event Simulation 18 ; Fuzzy Bayesian System Reliability 19 ; Line Sampling 20 ; Monte Carlo Simulation Method ( 21 ]); Subset Simulation 22 ; Weighted Average Simulation. 23 Although the two categories of methods, as described above, have been successfully developed and applied for reliability assessment of complex systems, these methods lack the capability of achieving a closer adherence to INs. These methods generally ignore the valuable characteristics of INs that can be inferred through their topological properties.…”