Details
| Original language | English |
|---|---|
| Article number | 113487 |
| Journal | Computer Methods in Applied Mechanics and Engineering |
| Volume | 373 |
| Early online date | 21 Oct 2020 |
| Publication status | Published - 1 Jan 2021 |
Abstract
This contribution presents a general approach for solving structural design problems formulated as a class of nonlinear constrained optimization problems. A Two-Phase approach based on Bayesian model updating is considered for obtaining the optimal designs. Phase I generates samples (designs) uniformly distributed over the feasible design space, while Phase II obtains a set of designs lying in the vicinity of the optimal solution set. The equivalent model updating problem is solved by the transitional Markov chain Monte Carlo method. The proposed constraint-handling approach is direct and does not require special constraint-handling techniques. The population-based stochastic optimization algorithm generates a set of nearly optimal solutions uniformly distributed over the vicinity of the optimal solution set. The set of optimal solutions provides valuable sensitivity information. In addition, the proposed scheme is a useful tool for exploration of complex feasible design spaces. The general approach is applied to an important class of problems. Specifically, reliability-based design optimization of structural dynamical systems under stochastic excitation. Numerical examples are presented to evaluate the effectiveness of the proposed design scheme.
Keywords
- Constrained optimization, Feasible design space, Markov sampling method, Meta-models, Reliability-based design, Stochastic optimization
ASJC Scopus subject areas
- Engineering(all)
- Computational Mechanics
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
- Physics and Astronomy(all)
- General Physics and Astronomy
- Computer Science(all)
- Computer Science Applications
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In: Computer Methods in Applied Mechanics and Engineering, Vol. 373, 113487, 01.01.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A general two-phase Markov chain Monte Carlo approach for constrained design optimization
T2 - Application to stochastic structural optimization
AU - Jensen, H.
AU - Jerez, D.
AU - Beer, M.
N1 - Funding Information: The research reported here was supported in part by CONICYT (National Commission for Scientific and Technological Research) under grant number 1200087 . Also, this research has been supported by CONICYT and DAAD under CONICYT-PFCHA/Doctorado Acuerdo Bilateral DAAD Becas Chile/ 2018-62180007 . In addition, this research has been implemented under the PAC (Programa Asistente Cientifico 2017)-UTFSM program. These supports are gratefully acknowledged by the authors.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - This contribution presents a general approach for solving structural design problems formulated as a class of nonlinear constrained optimization problems. A Two-Phase approach based on Bayesian model updating is considered for obtaining the optimal designs. Phase I generates samples (designs) uniformly distributed over the feasible design space, while Phase II obtains a set of designs lying in the vicinity of the optimal solution set. The equivalent model updating problem is solved by the transitional Markov chain Monte Carlo method. The proposed constraint-handling approach is direct and does not require special constraint-handling techniques. The population-based stochastic optimization algorithm generates a set of nearly optimal solutions uniformly distributed over the vicinity of the optimal solution set. The set of optimal solutions provides valuable sensitivity information. In addition, the proposed scheme is a useful tool for exploration of complex feasible design spaces. The general approach is applied to an important class of problems. Specifically, reliability-based design optimization of structural dynamical systems under stochastic excitation. Numerical examples are presented to evaluate the effectiveness of the proposed design scheme.
AB - This contribution presents a general approach for solving structural design problems formulated as a class of nonlinear constrained optimization problems. A Two-Phase approach based on Bayesian model updating is considered for obtaining the optimal designs. Phase I generates samples (designs) uniformly distributed over the feasible design space, while Phase II obtains a set of designs lying in the vicinity of the optimal solution set. The equivalent model updating problem is solved by the transitional Markov chain Monte Carlo method. The proposed constraint-handling approach is direct and does not require special constraint-handling techniques. The population-based stochastic optimization algorithm generates a set of nearly optimal solutions uniformly distributed over the vicinity of the optimal solution set. The set of optimal solutions provides valuable sensitivity information. In addition, the proposed scheme is a useful tool for exploration of complex feasible design spaces. The general approach is applied to an important class of problems. Specifically, reliability-based design optimization of structural dynamical systems under stochastic excitation. Numerical examples are presented to evaluate the effectiveness of the proposed design scheme.
KW - Constrained optimization
KW - Feasible design space
KW - Markov sampling method
KW - Meta-models
KW - Reliability-based design
KW - Stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=85093679937&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2020.113487
DO - 10.1016/j.cma.2020.113487
M3 - Article
AN - SCOPUS:85093679937
VL - 373
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0045-7825
M1 - 113487
ER -