A Random Field Method for Time-Dependent Reliability Analysis with Random and Interval Variables


In many engineering applications, both random and interval variables exist. Some of the random variables may also vary over time. As a result, the reliability of a component not only decreases with time but also resides in an interval. Evaluating the time-dependent reliability bounds is a challenging task because of the intensive computational demand. This research develops a method that treats a time-dependent random response as a random field with respect to both intervals and time. Consequently, random field methodologies can be used to estimate the worse-case time-dependent reliability. The method employs the first-order reliability method, which results in a Gaussian random field for the response with respect to intervals and time. The Kriging method and Monte Carlo simulation are then used to estimate the worse-case reliability without calling the original limit-state function. Good efficiency and accuracy are demonstrated through examples.

Meeting Name

ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2016: Aug. 21-24, Charlotte, NC)


Mechanical and Aerospace Engineering

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Computer aided design; Design; Gaussian distribution; Intelligent systems; Monte Carlo methods; Reliability; Structural analysis; Computational demands; Engineering applications; First order reliability methods; Gaussian random fields; Interval variable; Limit state functions; Time dependent reliability; Time dependent reliability analysis; Reliability analysis

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2016 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

01 Aug 2016