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Recently, the sustainability of traditional technologies employed in critical infrastructure brings a serious challenge for our society. In order to make decisions related with safety of critical infrastructure, the values of accidental risk are becoming relevant points for discussion. However the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and deal with high amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI). Therefore, this paper aims to investigate and compare AI algorithms for risk assessment. These algorithms are classified mainly into Expert Systems, Artificial Neural Networks and Hybrid intelligent Systems. This paper explains the principles of each classification system, as well as its applications in safety. Lately, this paper performs a comparative analysis of three representative techniques, such as Fuzzy-Expert System, Neural Networks, and Adaptive Neuro Fuzzy Inference System.
Guzman et al. (Thu,) studied this question.