Time-Dependent Reliability Analysis by a Sampling Approach to Extreme Values of Stochastic ProcessesProceedings of the ASME Design Engineering Technical Conference
AbstractMaintaining 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.
Meeting NameProceedings of the ASME Design Engineering Technical Conference (2012, Chicago, IL)
Department(s)Mechanical and Aerospace Engineering
Document TypeArticle - Conference proceedings
Rights© 2012 American Society of Mechanical Engineers (ASME), All rights reserved.
Citation InformationZhen Hu and Xiaoping Du. "Time-Dependent Reliability Analysis by a Sampling Approach to Extreme Values of Stochastic Processes" Proceedings of the ASME Design Engineering Technical Conference (2012)
Available at: http://works.bepress.com/xiaoping-du/91/