@Article{cmc.2025.067143,
AUTHOR = {Habib Hadj-Mabrouk},
TITLE = {Expert System Based on Ontology and Interpretable Machine Learning to Assist in the Discovery of Railway Accident Scenarios},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {3},
PAGES = {4399--4430},
URL = {http://www.techscience.com/cmc/v84n3/63204},
ISSN = {1546-2226},
ABSTRACT = {A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational, maintenance, and feedback phases following railway incidents or accidents. These approaches exploit railway safety data once the transport system has received authorization for commissioning. However, railway standards and regulations require the development of a safety management system (SMS) from the specification and design phases of the railway system. This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to improving the development of the SMS. Unlike some learning methods, the proposed approach, which is dedicated in particular to safety assessment bodies, is based on semi-supervised learning carried out in close collaboration with safety experts who contributed to the development of a database of potential accident scenarios (learning example database) relating to the risk of rail collision. The proposed decision support is based on the use of an expert system whose knowledge base is automatically generated by inductive learning in the form of an association rule (rule base) and whose main objective is to suggest to the safety expert possible hazards not considered during the development of the SMS to complete the initial hazard register.},
DOI = {10.32604/cmc.2025.067143}
}