Development of a risk-based method for predicting the severity of potential fire accidents in road tunnels based on real-time data

Fire incidents are considered serious events for road tunnel safety because they can evolve into catastrophic accidents. Bearing in mind that tunnels constitute critical infrastructure elements of road systems, risk assessment has been employed to prepare tunnels to deal with such incidents. However, if an incident occurs, an adequate response is also related to the information about the particular event. To this respect, a novel risk-based method is proposed to support tunnel operators in assessing the criticality of potential fire incidents by using real-time data. The structure of the proposed method is as follows. Initially, the backlayering that determines the criticality of an incident is examined and the stochastic parameters of the system that affect backlayering are identified. Subsequently, multiple simulations are performed by changing the examined parameters randomly and thus the relation between backlayering and those parameters arises. As a result, the developed relation provided with real-time data can estimate the potential severity of any incident occurring in real time. The outcome facilitates tunnel operators to predict promptly the potential severity of fires and make better-informed decisions. This will allow a more efficient operation of the control room of the tunnel. An illustrative case is presented to showcase the utilisation of the proposed method.