An Intelligent Railway Safety Risk Assessment Support System for Railway Operation and Maintenance Analysis

Min An*, Wanchang Lin, Sheng Huang
School of Civil Engineering, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.

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© 2013 Anet al;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Civil Engineering, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; Tel: +44-121-414-5146; Fax: +44-121-414-3698; E-mail:


The paper presents the development of an intelligent railway safety risk assessment based support system. The proposed method can evaluate qualitative and quantitative safety risk data and information in a uniform manner for railway safety risk assessment. It permits the safety risk analysts to assess the risks associated with the failure modes directly using linguistic terms, i.e. qualitative descriptors. The proposed intelligent railway safety risk assessment system is capable of assessing the risks at component level, sub-system level and system level. It can assess not only “hard” risks (e.g. risks of a system), but also “soft” risks (e.g. staff risks). The outcomes of safety risk assessment are represented in two formats, risk score and risk category with a belief of percentage, which provide very useful safety risk information to railway designers, operators, engineers and maintainers for risk response decision making. An illustrative example of staff risk assessment in a railway depot is used to demonstrate the proposed intelligent railway safety risk assessment system. The results indicate that by using the proposed system, risks associated with a railway depot can be assessed effectively and efficiently.

Keywords: Railway safety, safety risk assessment, fuzzy reasoning approach, qualitative descriptors, staff safety risk assessment.