Global sensitivity analysis of input variables for a train accident risk model

Reif, Monika Ulrike; Weng, Joanna; Zaugg, Christoph

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Global sensitivity analysis of input variables for a train accident risk model
Authors:	Reif, Monika Ulrike Weng, Joanna Zaugg, Christoph
et. al:	No
Proceedings:	Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference
Editors of the parent work:	Baraldi, Piero Di Maio, Francesco Zio, Enrico
Page(s):	4923
Pages to:	4928
Conference details:	30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15), Venice, Italy, 1-5 November 2020
Issue Date:	2020
Publisher / Ed. Institution:	Research Publishing
Publisher / Ed. Institution:	Singapore
ISBN:	978-981-14-8593-0
Language:	English
Subjects:	Sensitivity analysis; Uncertainty quantification; Sobol decomposition; Probabilistic risk assessment
Subject (DDC):	363: Environmental and security problems
Abstract:	Safe transportation of hazardous materials by rail is an important issue in Switzerland. This study analyzes an existing model for the risk of transport of hazardous materials via Swiss railways, in collaboration with the Swiss Federal Office for the Environment. The model is the basis for the risk calculation of hazards for persons for all railway transports of hazardous materials in Switzerland and is published by the Swiss Federal Office of Transport. It includes 155 input variables estimated with different uncertainties. The objective of this study is to determine which input variables possess the strongest influence on the model output (the risk) and should therefore be determined with higher accuracy. To achieve this objective, different sensitivity analysis methods as suggested by Borgonovo are compared. The risk model is implemented in Maple and the Sobol decomposition is used for a global sensitivity analysis of the input variables. The Sobol method is a variance-based sensitivity analysis that decomposes the variance of the output of the model into contributions due to input variables or sets of input variables. The Sobol indices are calculated analytically by evaluating various integrals in the decomposition. In addition, the stability of the method is investigated by using different ranges of the input variables. As a first cross check, the partial derivatives of all input variables are calculated for the same model. As a second cross check, an independent analysis in Matlab is carried out, based on Monte Carlo simulation of the input variables within their uncertainty range. The results are stable and consistent among all methods and will be used by the Swiss Federal Office for the Environment to optimize the estimation of the input variables of this risk model.
URI:	https://www.rpsonline.com.sg/proceedings/esrel2020/pdf/3879.pdf https://digitalcollection.zhaw.ch/handle/11475/22028
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Mathematics and Physics (IAMP)
Published as part of the ZHAW project:	Relevanz von Risikomodellparameter
Appears in collections:	Publikationen School of Engineering

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Reif, M. U., Weng, J., & Zaugg, C. (2020). Global sensitivity analysis of input variables for a train accident risk model [Conference paper]. In P. Baraldi, F. Di Maio, & E. Zio (Eds.), Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (pp. 4923–4928). Research Publishing. https://www.rpsonline.com.sg/proceedings/esrel2020/pdf/3879.pdf

Reif, M.U., Weng, J. and Zaugg, C. (2020) ‘Global sensitivity analysis of input variables for a train accident risk model’, in P. Baraldi, F. Di Maio, and E. Zio (eds) Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference. Singapore: Research Publishing, pp. 4923–4928. Available at: https://www.rpsonline.com.sg/proceedings/esrel2020/pdf/3879.pdf.

M. U. Reif, J. Weng, and C. Zaugg, “Global sensitivity analysis of input variables for a train accident risk model,” in Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference, 2020, pp. 4923–4928. [Online]. Available: https://www.rpsonline.com.sg/proceedings/esrel2020/pdf/3879.pdf

REIF, Monika Ulrike, Joanna WENG und Christoph ZAUGG, 2020. Global sensitivity analysis of input variables for a train accident risk model. In: Piero BARALDI, Francesco DI MAIO und Enrico ZIO (Hrsg.), Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference [online]. Conference paper. Singapore: Research Publishing. 2020. S. 4923–4928. ISBN 978-981-14-8593-0. Verfügbar unter: https://www.rpsonline.com.sg/proceedings/esrel2020/pdf/3879.pdf

Reif, Monika Ulrike, Joanna Weng, and Christoph Zaugg. 2020. “Global Sensitivity Analysis of Input Variables for a Train Accident Risk Model.” Conference paper. In Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference, edited by Piero Baraldi, Francesco Di Maio, and Enrico Zio, 4923–28. Singapore: Research Publishing. https://www.rpsonline.com.sg/proceedings/esrel2020/pdf/3879.pdf.

Reif, Monika Ulrike, et al. “Global Sensitivity Analysis of Input Variables for a Train Accident Risk Model.” Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference, edited by Piero Baraldi et al., Research Publishing, 2020, pp. 4923–28, https://www.rpsonline.com.sg/proceedings/esrel2020/pdf/3879.pdf.