Safety assessment using computer experiments and surrogate modeling: Railway vehicle safety and track quality indices

Neves Costa, João, Ambrósio, Jorge, Andrade, António R. and Daniel Frey

Reliability Engineering and System Safety, 2023, vol. 229, issue C

Abstract: Mathematical modeling and advances in computation allow exploring multiple scenarios and studying the reliability and safety of transportation systems. Although track geometry directly impacts vehicle safety, the track quality indices used by infrastructure managers to assess tracks seldom consider vehicle dynamics. This work provides a design and analysis of computer experiments framework to model the relationships between track quality and vehicle safety. The framework considers input selection and pre-processing, vehicle responses and post-processing, input screening, surrogate modeling, sensitivity analysis, and safety assessment. This approach allows studying how track geometry parameters and other variables influence safety quantities. The framework is demonstrated with a case study that combines two European standards: the standard for track geometry quality, EN 13848, and the standard for vehicle acceptance, EN 14363. The case study considers different vehicle types, vehicle speed, track curvature, track flexibility, and track irregularities. The results show, for each safety quantity, which inputs are relevant. In particular, the sensitivity analysis indicates two influential inputs not considered in EN 13848 that could help assess track condition. Finally, an example illustrates how these surrogates can be used to find which safety quantities govern safety and define track geometry limits directly linked to vehicle safety.

Keywords: Design of experiments; Computer experiments; Gaussian processes; Track geometry quality; Rail vehicle safety (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004732

DOI: 10.1016/j.ress.2022.108856

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