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

Cheng, Jin

doi:10.1016/j.jcsr.2010.03.007

Cited by 53 publications

(14 citation statements)

References 8 publications

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“…GA is an efficient method for solving complex 432 problems of optimization by simulating the biological evolution of the survival of the fittest 433 using three major processes: selection, crossover and mutation. The GA method has been 434 extensively adopted in structural optimization design (Cheng 2010 …”

Section: Shrinkage 393mentioning

confidence: 99%

Modeling for Assessment of Long-Term Behavior of Prestressed Concrete Box-Girder Bridges

Huang

Pilakoutas

2018

J. Bridge Eng.

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Large-span PC box girder bridges suffer excessive vertical deflections and 10 cracking. Recent serviceability failures in china show that, the current modelling approach of 11 the Chinese standard (JTG D62) fails to accurately predict long term deformations of large 12 box girder bridges. This hinders the efforts of inspectors to conduct satisfactory structural 13 assessments and make decisions on potential repair and strengthening. 14 This study presents a model updating approach aiming to assess the models used in JTG D62

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Section: Shrinkage 393mentioning

confidence: 99%

Modeling for Assessment of Long-Term Behavior of Prestressed Concrete Box-Girder Bridges

Huang

Pilakoutas

2018

J. Bridge Eng.

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“…Optimum design of steel space frames including soil -structure interaction was presented in the article [8]. The subjects of optimization were also semi-rigid steel frames in the work [9] and steel truss arch bridges in the work [10]. Monolithic reinforced concrete structure was optimized in the article [11] and FRP-confined concrete columns in the work [12].…”

Section: A R C H I T E C T U R E C I V I L E N G I N E E R I N G E N V I R O N M E N Tmentioning

confidence: 99%

Optimization of Trusses With Self-Adaptive Approach In Genetic Algorithms

Grygierek

2016

Architecture, Civil Engineering, Environment

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This paper presents a genetic algorithm method for the optimization of the weight of steel truss structures. In the method of genetic algorithm integer encoding of a discrete set of design variables and novel self-adaptive method based on fuzzy logic mechanism are applied for improving the quality and speed of optimization. Self-adaptive method is applied simultaneously in the selection of chromosomes and to control basic parameters of genetic algorithm. The algorithm proposed in the work was tested on the examples of optimization of steel trusses. Obtained results proved the effectiveness of genetic algorithm in relation to classical genetic algorithm.

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“…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. A simulated annealing method and sequential linear method 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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Optimum design of steel truss arch bridges using a hybrid genetic algorithm

Cited by 53 publications

References 8 publications

Modeling for Assessment of Long-Term Behavior of Prestressed Concrete Box-Girder Bridges

Modeling for Assessment of Long-Term Behavior of Prestressed Concrete Box-Girder Bridges

Optimization of Trusses With Self-Adaptive Approach In Genetic Algorithms

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

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