Contribution of artificial intelligence to the assessment of rail safety

Habib Hadj-Mabrouk (1)

1 IFSTTAR/DS - Direction scientifique

This article describes a contribution to improving the usual safety analysis methods used in the certification of railway transport systems. The methodology is based on the complementary and simultaneous use of knowledge acquisition and machine learning. We used the ACASYA software environment to support the safety analysis aid methodology. ACASYA aims to provide experts with suggestions of potential failures which have not been considered by the manufacturer and which are capable of jeopardizing the safety of a new rail transport system. ACASYA consists of two main modules: CLASCA and EVALSCA, respectively dedicated to the classification and evaluation of accident scenarios. CLASCA is an inductive, incremental and interactive learning system. EVALSCA, built around a learning system called CHARADE, aims to provide experts with suggestions of potential failures which have not been considered by the manufacturer and which are capable of jeopardizing the safety of a new rail transport system.

Mots clés: Accident scenarios; Knowledge acquisition; Machine learning; Rail transport; Risk assessment; Safety; Artificial intelligence

Domaines: Intelligence artificielle [cs.AI]; Machine Learning [stat.ML]; Informatique [cs]; Risques

Habib Hadj-Mabrouk. Contribution of artificial intelligence to the assessment of rail safety. Stephenson Conference: Research for Railways 2017, Apr 2017, Londres, United States. pp.149-160. ⟨hal-02424636⟩