Sector of Industrial Management and Operations Research, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece.
Sector of Industrial Management and Operations Research, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece.
Environ Res. 2020 Oct;189:109895. doi: 10.1016/j.envres.2020.109895. Epub 2020 Jul 12.
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.
火灾被认为是道路隧道安全的严重事件，因为它们可能会演变成灾难性事故。考虑到隧道是道路系统的关键基础设施元素，风险评估已被用于使隧道能够应对此类事件。然而，如果发生事故，充分的应对措施也与特定事件的信息有关。在这方面，提出了一种新的基于风险的方法，通过使用实时数据来支持隧道运营商评估潜在火灾事故的严重性。该方法的结构如下。首先，检查确定事件严重性的后分层，并确定影响后分层的系统随机参数。随后，通过随机改变检查的参数来执行多次模拟，从而产生后分层与这些参数之间的关系。结果，与实时数据一起开发的关系可以实时估计任何发生的事件的潜在严重程度。该结果有助于隧道运营商及时预测火灾的潜在严重程度并做出更明智的决策。这将允许隧道控制室更有效地运行。展示了一个说明性案例，以展示所提出方法的利用。
