TY - JOUR

T1 - On the optimal strategy of stochastic-based reliability assessment of railway bridges for high-speed trains

AU - Hirzinger, Benjamin

AU - Adam, Christoph

AU - Salcher, Patrick

AU - Oberguggenberger, Michael

N1 - Funding Information: Open access funding provided by University of Innsbruck and Medical University of Innsbruck. The computational results presented have been achieved (in part) using the HPC infrastructure LEO of the University of Innsbruck.

PY - 2019/7/1

Y1 - 2019/7/1

N2 - The scope of this paper is to evaluate the performance and computational efficiency of various stochastic simulation methods for a stochastic based reliability assessment of railway bridges subjected to high-speed trains. Depending on the degree of sophistication, application of crude Monte Carlo simulation to a realistic mechanical model of the uncertain bridge-train interacting dynamical system can be prohibitively expensive. Thus, three alternative stochastic methods, i.e. line sampling, subset simulation, and asymptotic sampling, are tested on two example problems. These examples represent two classes of bridges with different dynamic response characteristics. While in the one class of bridges distinctive resonance peaks govern the dynamic peak response, the random response amplification of the second group of bridges is primarily induced by track irregularities. The studies are conducted on a simplified mechanical model, composed of a plain beam representing the bridge and a planar mass-spring-damper system representing the train. This modeling strategy captures the fundamental characteristics of dynamic bridge-train interaction, and thus, facilitates the desired assessment of the stochastic methods with reasonable computational effort. It is shown that both line sampling and subset simulation reduce significantly the computational expense for the first class of bridges, while maintaining the accuracy of the predicted bridge reliability. To ensure accuracy and efficiency, these methods need to be modified when applied to systems where track irregularities dominate the random response. For the latter class of bridges, subset simulation proved to be a suitable method for assessing the reliability of this dynamic interacting system when appropriately modified.

AB - The scope of this paper is to evaluate the performance and computational efficiency of various stochastic simulation methods for a stochastic based reliability assessment of railway bridges subjected to high-speed trains. Depending on the degree of sophistication, application of crude Monte Carlo simulation to a realistic mechanical model of the uncertain bridge-train interacting dynamical system can be prohibitively expensive. Thus, three alternative stochastic methods, i.e. line sampling, subset simulation, and asymptotic sampling, are tested on two example problems. These examples represent two classes of bridges with different dynamic response characteristics. While in the one class of bridges distinctive resonance peaks govern the dynamic peak response, the random response amplification of the second group of bridges is primarily induced by track irregularities. The studies are conducted on a simplified mechanical model, composed of a plain beam representing the bridge and a planar mass-spring-damper system representing the train. This modeling strategy captures the fundamental characteristics of dynamic bridge-train interaction, and thus, facilitates the desired assessment of the stochastic methods with reasonable computational effort. It is shown that both line sampling and subset simulation reduce significantly the computational expense for the first class of bridges, while maintaining the accuracy of the predicted bridge reliability. To ensure accuracy and efficiency, these methods need to be modified when applied to systems where track irregularities dominate the random response. For the latter class of bridges, subset simulation proved to be a suitable method for assessing the reliability of this dynamic interacting system when appropriately modified.

KW - Bridge-train interaction

KW - Probability of failure

KW - Reliability assessment

KW - Stochastic simulations

UR - http://www.scopus.com/inward/record.url?scp=85067954964&partnerID=8YFLogxK

U2 - 10.1007/s11012-019-00999-0

DO - 10.1007/s11012-019-00999-0

M3 - Article

AN - SCOPUS:85067954964

VL - 54

SP - 1385

EP - 1402

JO - MECCANICA

JF - MECCANICA

SN - 0025-6455

IS - 9

ER -