Time-Dependent Reliability Analysis by a Sampling Approach to Extreme Values of Stochastic Processes
Maintaining high accuracy and efficiency is a challenging issue in time-dependent reliability analysis. In this work, an accurate and efficient method is proposed for limit-state functions with the following features: The limit-state function is implicit with respect to time, and its input contains stochastic processes; the stochastic processes include only general strength and stress variables, or the limit-state function is monotonic to these stochastic processes. The new method employs random sampling approaches to estimate the distributions of the extreme values of the stochastic processes. The extreme values are then used to replace the corresponding stochastic processes, and consequently the time-dependent reliability analysis is converted into its time-invariant counterpart. The commonly used time-invariant reliability method, the First Order Reliability Method, is then applied for the time-variant reliability analysis. The results show that the proposed method significantly improves the accuracy and efficiency of time-dependent reliability analysis. © 2012 by ASME.
Z. Hu and X. Du, "Time-Dependent Reliability Analysis by a Sampling Approach to Extreme Values of Stochastic Processes," Proceedings of the ASME Design Engineering Technical Conference, American Society of Mechanical Engineers (ASME), Jan 2012.
The definitive version is available at https://doi.org/10.1115/DETC2012-70132
Proceedings of the ASME Design Engineering Technical Conference (2012, Chicago, IL)
Mechanical and Aerospace Engineering
Article - Conference proceedings
© 2012 American Society of Mechanical Engineers (ASME), All rights reserved.