Perpustakaan Digital ITB

In the design, development, and operation of a rail transport system, all the actors involved use one or more safety methods to identify hazardous situations, the causes of hazards, potential accidents, and the severity of the consequences that would result. The main objective is to justify and ensure that the design architecture of the transportation system is safe and presents no particular risk to users or the environment. As part of this process of certification, domain experts are responsible for reviewing the safety of the system, and are being brought in to imagine new scenarios of potential accidents to ensure the exhaustiveness of such safety studies. One of the difficulties in this process is to determine abnormal scenarios that could lead to a particular potential accident. This is the fundamental point that motivated the present work, whose objective is to develop tools to assist certification experts in their crucial task of analyzing and evaluating railway safety. However, the type of reasoning (inductive, deductive, by analogy, etc.) used by certification experts as well as the very nature of the knowledge manipulated in this certification process (symbolic, subjective, evolutionary, empirical, etc.) justify that conventional computer solutions cannot be adopted; the use of artificial intelligence (AI) methods and techniques helps to understand the problem of safety analysis and certification of high-risk systems such as guided rail transport systems. To help experts in this complex process of evaluating safety studies, we decided to use AI techniques and in particular machine learning to systematize, streamline, and strengthen conventional approaches used for safety analysis and certification