Reliability-Based Design Optimization under Stationary Stochastic Process Loads


Time-dependent reliability-based design ensures the satisfaction of reliability requirements for a given period of time, but with a high computational cost. This work improves the computational efficiency by extending the sequential optimization and reliability analysis (SORA) method to time-dependent problems with both stationary stochastic process loads and random variables. The challenge of the extension is the identification of the most probable point (MPP) associated with time-dependent reliability targets. Since a direct relationship between the MPP and reliability target does not exist, this work defines the concept of equivalent MPP, which is identified by the extreme value analysis and the inverse saddlepoint approximation. With the equivalent MPP, the time-dependent reliability-based design optimization is decomposed into two decoupled loops: deterministic design optimization and reliability analysis, and both are performed sequentially. Two numerical examples are used to show the efficiency of the proposed method.


Mechanical and Aerospace Engineering

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Computational efficiency; Design; Efficiency; Inverse problems; Machine design; Numerical methods; Optimization; Random processes; Reliability; Stochastic systems; Extreme value analysis; Reliability requirements; Reliability-based design optimization; Saddle-point approximation; Sequential optimization; Stationary stochastic process; Time dependent reliability; Time-dependent problem; Reliability analysis

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


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© 2016 Taylor & Francis, All rights reserved.

Publication Date

01 Aug 2016