Optimal design of arch bridges by integrating genetic algorithms and the mechanism method

Peng, Di; Fairfield, C. A.

doi:10.1016/s0141-0296(97)00124-7

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…In the present study a genetic algorithm is presented which is somewhat different from the one described in [6] regarding the genetic operators and process. These differences include (1) the use of real-coded method, instead of a simple binary coding method;…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Optimum design of steel truss arch bridges using a hybrid genetic algorithm

Cheng

2010

Journal of Constructional Steel Research

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Section: Genetic Algorithmmentioning

confidence: 99%

“…Although much work has been performed in this area, the application of the GA to the optimization of arch bridges is quite limited. An attempt was made to apply GA to optimal design of stone masonry arch bridges [6]. In their study, the problem was solved by a simple genetic algorithm with reproduction, crossover, and mutation operators.…”

Section: Introductionmentioning

confidence: 99%

Optimum design of steel truss arch bridges using a hybrid genetic algorithm

Cheng

2010

Journal of Constructional Steel Research

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“…DDO allows one to find the configuration of a structure that is optimum in terms of mechanics and weight. An integrated design optimization combining the mechanism method with genetic algorithms was used for arch designs . A hybrid algorithm method which integrates the concepts of the genetic algorithm and the finite element method was proposed and demonstrated by the optimization of a long‐span steel‐truss arch bridge.…”

Section: Introductionmentioning

confidence: 99%

“…An integrated design optimization combining the mechanism method with genetic algorithms was used for arch designs. 2 A hybrid algorithm method which integrates the concepts of the genetic algorithm and the finite element method 3 was proposed and demonstrated by the optimization of a long-span steel-truss arch bridge. A simulated annealing method 4,5 and sequential linear method 6,7 were also presented for the optimal design problems.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Structural System Reliability-Based Design Optimization and its Application to Trusses

Liu

Yin

2015

Qual. Reliab. Engng. Int.

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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.

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“…Several authors (Bucher and Frangopol, 2006) had investigated both of DDO and RBDO. The objective of DDO is to find the structural configurations in such a way that optimum parameters are balanced in terms of mechanics and weight (Peng and Fairfield, 1999). However, there are no simple relationships between the design variables and structural responses.…”

Section: Introductionmentioning

confidence: 99%

System reliability-based optimisation for truss structures using genetic algorithm and neural network

Liu

Noori

et al. 2014

IJRS

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Optimum structures must have adequate resistance against external random loads. Since most truss structures involve a series of failure processes, it is necessary to develop system reliability analyses for the optimum design of truss structures. In this paper, a hybrid method of system reliability-based design optimisation (SRBDO) is proposed by combining genetic algorithms (GAs) and radial basis functions (RBFs) neural networks. The proposed method is applied to truss structures, and then the validity is demonstrated through two specific examples. Detailed discussions for the failure sequences such as buckling failure and bending failure are presented. It is concluded that the structural weight increases significantly with the increase of the target 52 Y. Liu et al.system reliability index or the coefficient of variation of design parameters. Results of two optimisation schemes of the steel truss girder show that the cross-sectional areas of the beams are decreased and those of web members are increased.Reference to this paper should be made as follows: Liu, Y., Lu, N., Noori, M. and Yin, X. (2014) 'System reliability-based optimisation for truss structures using genetic algorithm and neural network', Int.

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Optimal design of arch bridges by integrating genetic algorithms and the mechanism method

Cited by 12 publications

References 5 publications

Optimum design of steel truss arch bridges using a hybrid genetic algorithm

Optimum design of steel truss arch bridges using a hybrid genetic algorithm

A Hybrid Method for Structural System Reliability-Based Design Optimization and its Application to Trusses

System reliability-based optimisation for truss structures using genetic algorithm and neural network

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