Reliability-based optimization of stochastic systems using line search
✍ Scribed by H.A. Jensen; M.A. Valdebenito; G.I. Schuëller; D.S. Kusanovic
- Book ID
- 104011873
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 506 KB
- Volume
- 198
- Category
- Article
- ISSN
- 0045-7825
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✦ Synopsis
excitation a b s t r a c t This contribution presents an approach for solving reliability-based optimization problems involving structural systems under stochastic loading. The associated reliability problems to be solved during the optimization process are high-dimensional (1000 or more random variables). A standard gradientbased algorithm with line search is used in this work. Subset simulation is adopted for the purpose of estimating the corresponding failure probabilities. The gradients of the failure probability functions are estimated by an approach based on the local behavior of the performance functions that define the failure domains. Numerical results show that only a moderate number of reliability estimates has to be performed during the entire design process. Two numerical examples showing the effectiveness of the approach reported herein are presented.
📜 SIMILAR VOLUMES
The concept of the gradient of a performance criterion for a stochastic control system m connection with feedback-type con.trollers is developed. It is suggested that the inherent difficulty of dimensionality which is encountered in making optimal designs of feedback-type controllers can be overcome
The modified random-to-pattern search (MRPS) algorithm, developed by the authors for global optimization, is applied to find the global optimum of system cost of a complex system subject to constraints on system reliability. The global optimum solutions obtained by MRPS are compared with those obtai