Details
| Original language | English |
|---|---|
| Article number | 108042 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 162 |
| Early online date | 13 May 2021 |
| Publication status | Published - 1 Jan 2022 |
Abstract
In this work attention is directed to general structural optimization problems considering discrete–continuous design variables. The optimization problem is formulated as the minimization of an objective function subject to multiple design requirements. The mathematical programming statement is set into the framework of a Bayesian model updating problem. Constraints are handled directly within the proposed scheme, generating designs distributed over the feasible design space. Based on these samples, a set of designs lying in the vicinity of the optimal solution set is obtained. The Bayesian model updating problem is solved by an effective Markov chain Monte Carlo simulation scheme, where appropriate proposal distributions are introduced for the continuous and discrete design variables. The approach can efficiently estimate the sensitivity of the final design and constraints with respect to the design variables. In addition, the numerical implementation of the optimization algorithm depends on few control parameters. For illustration purposes, the general formulation is applied to an important class of problems, specifically, reliability-based design optimization of structural systems under stochastic excitation. Three numerical examples showing the effectiveness and potentiality of the approach reported herein are presented.
Keywords
- Bayesian updating, Discrete–continuous optimization, Feasible design space, Markov sampling method, Reliability-based optimization, Stochastic optimization
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 162, 108042, 01.01.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Structural synthesis considering mixed discrete–continuous design variables
T2 - A Bayesian framework
AU - Jensen, H. A.
AU - Jerez, D. J.
AU - Beer, M.
N1 - Funding Information: The research reported here was supported in part by CONICYT, Chile (National Commission for Scientific and Technological Research) under grant number 1200087 . Also, this research has been supported by CONICYT, Chile and DAAD, Germany 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 - 2022/1/1
Y1 - 2022/1/1
N2 - In this work attention is directed to general structural optimization problems considering discrete–continuous design variables. The optimization problem is formulated as the minimization of an objective function subject to multiple design requirements. The mathematical programming statement is set into the framework of a Bayesian model updating problem. Constraints are handled directly within the proposed scheme, generating designs distributed over the feasible design space. Based on these samples, a set of designs lying in the vicinity of the optimal solution set is obtained. The Bayesian model updating problem is solved by an effective Markov chain Monte Carlo simulation scheme, where appropriate proposal distributions are introduced for the continuous and discrete design variables. The approach can efficiently estimate the sensitivity of the final design and constraints with respect to the design variables. In addition, the numerical implementation of the optimization algorithm depends on few control parameters. For illustration purposes, the general formulation is applied to an important class of problems, specifically, reliability-based design optimization of structural systems under stochastic excitation. Three numerical examples showing the effectiveness and potentiality of the approach reported herein are presented.
AB - In this work attention is directed to general structural optimization problems considering discrete–continuous design variables. The optimization problem is formulated as the minimization of an objective function subject to multiple design requirements. The mathematical programming statement is set into the framework of a Bayesian model updating problem. Constraints are handled directly within the proposed scheme, generating designs distributed over the feasible design space. Based on these samples, a set of designs lying in the vicinity of the optimal solution set is obtained. The Bayesian model updating problem is solved by an effective Markov chain Monte Carlo simulation scheme, where appropriate proposal distributions are introduced for the continuous and discrete design variables. The approach can efficiently estimate the sensitivity of the final design and constraints with respect to the design variables. In addition, the numerical implementation of the optimization algorithm depends on few control parameters. For illustration purposes, the general formulation is applied to an important class of problems, specifically, reliability-based design optimization of structural systems under stochastic excitation. Three numerical examples showing the effectiveness and potentiality of the approach reported herein are presented.
KW - Bayesian updating
KW - Discrete–continuous optimization
KW - Feasible design space
KW - Markov sampling method
KW - Reliability-based optimization
KW - Stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=85110307848&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2021.108042
DO - 10.1016/j.ymssp.2021.108042
M3 - Article
AN - SCOPUS:85110307848
VL - 162
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 108042
ER -