A Hybrid Method for Structural System Reliability‐Based Design Optimization and its Application to Trusses. (19th February 2015)
- Record Type:
- Journal Article
- Title:
- A Hybrid Method for Structural System Reliability‐Based Design Optimization and its Application to Trusses. (19th February 2015)
- Main Title:
- A Hybrid Method for Structural System Reliability‐Based Design Optimization and its Application to Trusses
- Authors:
- Liu, Yang
Lu, Naiwei
Yin, Xinfeng - Abstract:
- Abstract : Most research studies on structural optimum design have focused on single‐objective optimization of deterministic structures, while little study has been carried out to address multi‐objective optimization of random structures. Statistical parameters and redundancy allocation problems should be considered in structural optimization. In order to address these problems, this paper presents a hybrid method for structural system reliability‐based design optimization (SRBDO) and applies it to trusses. The hybrid method integrates the concepts of the finite element method, radial basis function (RBF) neural networks, and genetic algorithms. The finite element method was used to compute structural responses under random loads. The RBF neural networks were employed to approximate structural responses for the purpose of replacing the structural limit state functions. The system reliabilities were calculated by Monte Carlo simulation method together with the trained RBF neural networks. The optimal parameters were obtained by genetic algorithms, where the system reliabilities were converted into penalty functions in order to address the constrained optimization. The hybrid method applied to trusses was demonstrated by two examples which were a typical 10‐bar truss and a steel truss girder structure. Detailed discussions and parameter analysis for the failure sequences such as web‐bucking failure and beam‐bending failure in the SRBDO were given. This hybrid method provides aAbstract : Most research studies on structural optimum design have focused on single‐objective optimization of deterministic structures, while little study has been carried out to address multi‐objective optimization of random structures. Statistical parameters and redundancy allocation problems should be considered in structural optimization. In order to address these problems, this paper presents a hybrid method for structural system reliability‐based design optimization (SRBDO) and applies it to trusses. The hybrid method integrates the concepts of the finite element method, radial basis function (RBF) neural networks, and genetic algorithms. The finite element method was used to compute structural responses under random loads. The RBF neural networks were employed to approximate structural responses for the purpose of replacing the structural limit state functions. The system reliabilities were calculated by Monte Carlo simulation method together with the trained RBF neural networks. The optimal parameters were obtained by genetic algorithms, where the system reliabilities were converted into penalty functions in order to address the constrained optimization. The hybrid method applied to trusses was demonstrated by two examples which were a typical 10‐bar truss and a steel truss girder structure. Detailed discussions and parameter analysis for the failure sequences such as web‐bucking failure and beam‐bending failure in the SRBDO were given. This hybrid method provides a new idea for SRBDO of trusses. Copyright © 2015 John Wiley & Sons, Ltd. … (more)
- Is Part Of:
- Quality and reliability engineering international. Volume 32:Number 2(2016:Mar.)
- Journal:
- Quality and reliability engineering international
- Issue:
- Volume 32:Number 2(2016:Mar.)
- Issue Display:
- Volume 32, Issue 2 (2016)
- Year:
- 2016
- Volume:
- 32
- Issue:
- 2
- Issue Sort Value:
- 2016-0032-0002-0000
- Page Start:
- 595
- Page End:
- 608
- Publication Date:
- 2015-02-19
- Subjects:
- design optimization -- system reliability -- genetic algorithm -- neural network -- failure sequence -- truss
Reliability (Engineering) -- Periodicals
Quality control -- Periodicals
High technology -- Periodicals
620.00452 - Journal URLs:
- http://www3.interscience.wiley.com/cgi-bin/jhome/3680 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/qre.1775 ↗
- Languages:
- English
- ISSNs:
- 0748-8017
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7168.137300
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 423.xml