Reliability-based design optimization of adhesive bonded steel–concrete composite beams with probabilistic and non-probabilistic uncertainties

Luo, Yangjun; Li, Alex; Kang, Zhan

doi:10.1016/j.engstruct.2011.02.040

Cited by 79 publications

(20 citation statements)

References 41 publications

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“…Such problems include the sizing of individual members (sizing optimization problems), the selection of the most efficient placing of structural members (topology optimization) or a combination of both in order to achieve a single or multiple goals at the same time (multiobjective optimization). The existing literature is rich of papers proposing and applying optimization algorithms in structural problems [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78].…”

Section: Application On Structural Optimizationmentioning

confidence: 99%

Assessment of the Effectiveness of Cabling System Configuration in Retrofitting Steel-Concrete Composite Buildings

Papavasileiou¹

2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Section: Application On Structural Optimizationmentioning

confidence: 99%

Assessment of the Effectiveness of Cabling System Configuration in Retrofitting Steel-Concrete Composite Buildings

Papavasileiou¹

2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…In this approach, the problem between reliability and optimisation is approximated into a series of deterministic problems. A sequential approximate programming approach is proposed to solve the mixed reliability index constraints and applied to a single span adhesive bonded steel-concrete composite beam with different loading cases (Luo et al, 2011). Stochastic Finite Element Analysis (SFEA) has also been developed to perform the probabilistic safety and optimal design assessment of steel cable-stayed bridges (Han and Lee, 2011).…”

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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“…An enhanced sequential optimization and reliability assessment was proposed to improve the computational efficiency for RBDO, and then a vehicle side impact example was applied to demonstrate the efficiency compared with the MPP method. The problems of reliability and optimization were simplified to be a series of deterministic problems, and then the algorithm was demonstrated by a single span adhesive bonded steel–concrete composite beam with different loading cases . Stochastic finite element analysis was applied to assess the probabilistic safety and optimal design of steel cable‐stayed bridges …”

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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Reliability-based design optimization of adhesive bonded steel–concrete composite beams with probabilistic and non-probabilistic uncertainties

Cited by 79 publications

References 41 publications

Assessment of the Effectiveness of Cabling System Configuration in Retrofitting Steel-Concrete Composite Buildings

Assessment of the Effectiveness of Cabling System Configuration in Retrofitting Steel-Concrete Composite Buildings

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

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

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