ROAD TUNNEL RESEARCH | FIRE SAFETY


tunnel. Collision rates are higher in tunnels with a high gradient. The corrective gradient factors determined in are used to derive the fire rate for the tunnel under study.8 Drivers’ eye movement and driving performance are


different during day and night, the accident rate and consequently the fire rate are different day and night. Therefore, the next influential factor in the frequency event tree is ‘The Time of the Incidents’, the second column. In the following, as the fire rate is varied by traffic conditions, i.e., accident rate in congestion is more than in free-flow traffic, traffic condition effect on fire rate is taken into account by considering the congested hours of the under-study tunnel, the third column. Furthermore, two accident types were considered in


this model, the fourth column. Incidents that include one vehicle are Type 1 and collisions that include more than one vehicle are Type 2. The probability of Types 1 and 2 incidents is derived from UK road data. The fifth column is ‘Vehicle Type’. The share of passenger cars, buses, vans, HGV, and trucks involved in fire incidents based on the tunnel fire data in PIARC 19999


and the traffic


composition of the studied tunnel is considered. The final fire frequency is calculated by multiplying


the fire rate for the tunnel under study by the columns below: ● Time of fire incident (Day/Night); ● Traffic condition (Congestion/Free flow); ● Accident type (Type 1/Type 2); ● Vehicle type.


The Quantitative Consequence Analysis lens The Quantitative Consequence Analysis calculates the number of casualties. In each scenario studied in this section a set of variables are considered, including tunnel geometry and infrastructures, different combinations of fire safety equipment, traffic density and composition, and various management procedures such as emergency ventilation strategies. The analysis comprises three parts: queue model, distribution model, and egress model. These are discussed below. When a fire happens, vehicles queue behind the


fire. The number of vehicles queueing in each lane and consequently the number of exposed tunnel users is calculated by taking into account tunnel closure time, queue formation speed, stopping distance between vehicles in the queue, fire source location, and the density of stopped vehicles in each lane. The exposed tunnel users are distributed in the queue


homogeneously in the distribution sub-model. In this sub-model, the agents’ gender is considered and both the longitudinal and lateral evacuation distances are calculated. The egress sub-model is a four-stage evacuation


process, a timeline model. Detection time depending on the detection system is the first stage. The second stage, the alarm stage, which is calculated considering the influence of provided safety systems in the tunnel,


such as smoke/ fire detection system and video/ radar incident detection system. Detection and alarm times are followed by the pre-movement time, including recognition time, response time, and the time to exit the vehicle. Considering the effect of people’s reactions on each


other and the smoke spread, people stuck behind the fire in the queue can be divided into three groups: direct accident witnesses; people out of the crash zone with no knowledge of the incident; and, those who are out of crash zone, influenced by escaping people. In the last stage, traveling time is calculated by


an innovative approach considering the influence of local toxic, thermal and visibility conditions on the walking speed10


. The required safe egress time (ASET) is


calculated by summing the times of these four stages. Detailed smoke propagation and evacuation


simulations are used to estimate the damage resulting from fires. If the mutual interaction between all influential factors is considered, a high level of accuracy can be achieved by Computational Fluid Dynamics (CFD) simulation. A three-dimensional CFD simulation (by FDS) is used to measure the fire consequences, such as temperature, heat and toxic gas concentrations at human height along the egress route. Design fires for the passenger car, bus, and HGVs were created by comparison of the experimental design fire data points recreated from experimental fire studies. A distinction is also made between fires with fast and


slow fire progression. For each fire scenario simulated by FDS, a one-dimensional longitudinal airflow model, developed in-house by LBA, is implemented to use the transient development of airflow velocity as a boundary condition in FDS. The survivability of evacuees is determined by calculating ‘Fractional Effective Doses’ (FED) concerning asphyxiation and hypothermia, the ‘Fractional Incapacitation Concentration’ (FIC), and comparison between the evacuation time (RSET) and the time when the tenability thresholds exceed their limits (ASET). The number of injuries is calculated by considering temperature as a threshold. Moreover, fire spread from the initial vehicle to adjacent vehicles causes casualties as well. The amount of radiation flux is measured at the location of each vehicle in the queue to find out whether the fire spread to them or not and the number of casualties regarding the spread of the fire is calculated. The number of vehicles with battery-electric drives


in road traffic has increased noticeably in recent years, which increases the frequency of accidents on open roads and in tunnels involving these vehicles. As a result, fire scenarios including Battery Electric Vehicles (BEV) and Electric buses are also included in LBAQRA.


Testing and Results The results of LBAQRA can be presented as a societal risk, which is determined as a combination of event frequency and consequences. This risk is usually represented by the F-N curve. Sensitivity analysis is also another way to present the results. Modeling a fire


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