Fuzzy Sensitivity Analysis of Structural Performance

Abstract

Despite the versatility and widespread application of fuzzy randomness in structural and mechanical engineering, less attention has been paid to the formulation of sensitivity analysis for this uncertainty model. In this research, a brief review of the application of sensitivity analyses in structural engineering is provided, and then the concept of local sensitivity analysis is developed for the fuzzy randomness theory. Several sensitivity tests based on the classical probability theory are extended to this uncertainty model, namely, Monte Carlo simulation (MCS), tornado diagram analysis (TDA), and first-order second-moment method (FOSM). The multidisciplinary application of these methods in engineering is shown using a numerical example, a truss structure, and finally, seismic performance evaluation of a framed structure from a full-scale experimental test. The way of visualizing the results is also provided, which helps the interpretation and better understanding. The results show that the established tools can provide detailed insight into the uncertainty of fuzzy random models. The formulated fuzzy local sensitivity can show how the output uncertainty is affected by the uncertainty of input parameters and the effectiveness of each parameter on the output variability. The provided visualization technique can show variability, the fuzziness of variability, and the order of most influential parameters. Furthermore, efficient methods such as TDA and FOSM can substantially reduce the computational time compared to the MCS while providing an acceptable trade-off for accuracy.