Application of evidential networks in quantitative analysis of railway accidents

Abstract

Currently, a high percentage of accidents in railway systems are accounted to human factors. As a consequence, safety engineers try to take into account this factor in risk assessment. However, human reliability data are very difficult to quantify, thus, qualitative methods are often used in railway system's risk assessments. Modeling of human errors through probabilistic approaches has shown some limitation concerning the quantification of qualitative aspects of human factors. The proposed article presents an original method to account for the human factor by using evidential networks and fault tree analysis.

Parallel verification of temporal properties using dynamic analysis

“…In this context, the considered software are critical and their verification is essential for avoiding collisions or nearcollisions [3]. To do this, a proprietary simulator allows the simulation of the train behaviours.…”

On the application of Valuation-Based Systems in the assessment of the probability bounds of Hazardous Material transportation accidents occurrence

“…In this work, we will use the belief functions theory (also called Dempster-Shafer (D-S) theory) which is a generalization of the Bayesian theory of subjective probability, and have proven very efficient in many applications of risk analysis (Demotier et al, 2006;Aguirre et al, 2013;Ortiz et al, 2013) and reliability studies (Simon et al, 2008;Sallak et al, 2013a,b). As explained by Shafer (1976): 'belief functions allow us to base degrees of belief for one question from probabilities for another.…”

Evidential Networks for Evaluating Predictive Reliability of Mechatronics Systems under Epistemic Uncertainties

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