Improved dynamic design method of ballasted high-speed railway bridges using surrogate-assisted reliability-based design optimization of dependent variables

R. Allahvirdizadeh, A. Andersson and R. Karoumi

Reliability Engineering and System Safety, 2023, vol. 238, issue C

Abstract: Operating high-speed trains imposes excessive vibrations to bridges raising concerns about their safety. In this context, it was shown that some conventional design methods such as those related to the running safety suffer from a vague scientific background questioning their reliability or optimality. Therefore, the current article is devoted to updating the conventional design methodology, using Reliability-Based Design Optimization (RBDO) to propose the minimum allowable mass and stiffness which assures satisfying the target reliability. These proposed minimum design values can conceptually replace the conventional partial safety factor-based design method for running safety without the need for dynamic analysis. If the mass and stiffness resulting from the control of other limit states meet the proposed minimum values, the desired target reliability for running safety will be assured. This is achieved by adaptively training Kriging meta-models as a surrogate for the computational models decoupling the RBDO problem. In this regard, a new stopping criteria is proposed using mis-classification ratio of the cross-validated model; which reduces the generalization error of the trained meta-model and consequently the estimated failure probability. Moreover, due to the dependence of the design variables, the Copula concept is used to refine the augmented space and reformulate the RBDO problem.

Keywords: Reliability-based design optimization; High-speed railway bridges; Bridge dynamics; Meta-modelling; Kriging; Adaptive sampling; Active learning; Dependent variables; Copula function (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:238:y:2023:i:c:s0951832023003204

DOI: 10.1016/j.ress.2023.109406

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