Enhancing Road Tunnel Risk Assessment with a Fuzzy System Based on the CREAM Methodology

Abstract

A great increase has been noticed in the number of road tunnels in Europe over the last decades. This can be attributed to the improvement of tunnel construction technology which has rendered tunnels a cost effective solution to connect steep mountainous regions and traverse urban areas. However, the increasing number of these infrastructures is a double-edged sword raising upfront an endogenous problem too, which is the severity of accidents that may occur in them. Accidents in road tunnels may lead to heavy consequences for the users, the infrastructure itself and the environment. Within this context the European Commission launched the Directive 2004/54/EC that sets basic requirements and suggests the implementation of a risk assessment in several cases apart from the measures imposed based on tunnel length and traffic volume. Since the EU Directive does not indicate the method for performing the risk assessment a wide range of methods have been proposed, most of them based on Quantitative Risk Assessment (QRA). Although the majority of current road tunnel QRAs assess physical aspects of the tunnel system and consider several hazards concerning the transportation of dangerous goods (DGs) through a tunnel, they do not take into account several organizational and human factors that can greatly affect the overall safety level of these critical infrastructures. To cope with this limitation this paper proposes a fuzzy logic system based on CREAM methodology in order to provide more sophisticated estimations of the tunnel operator’s performance in safety critical situations. This paper couples the results produced by the fuzzy logic system with the input parameters for a road tunnel QRA model (namely the OECD/PIARC DG-QRA Model). The results from the analysis reveal that the estimations of the tunnel operator’s performance produced by the fuzzy system significant affect the results of the road tunnel QRA. Therefore, it is deduced that the proposed fuzzy system can serve as a useful tool for the analyst to consider organizational and human factors so as to enhance the analysis and highlight the uncertainty related to human performance variability.