Inundation Risk Assessment of Underground Space Using Consequence-Probability Matrix

Abstract

Increases in the frequency and severity of extreme rainfall might cause catastrophic submergence of underground spaces. Therefore, it is essential to predict the flood risk for proactive design. This study presents the methodology of acquiring risk level considering both flood intensity and evacuation difficulty. The flood momentum was computed by a 2D hydraulic flow model, and the flood intensity (FI) was employed to evaluate the consequence of flooding. To investigate the level of evacuation difficulty, the spatial layout of rooms, together with walking speed, were considered in the risk analysis process. If stormwater runoff enters an underground space, zones far away from the inlet usually have low risk levels. However, when the level of evacuation difficulty was considered, the risk level was dependent on the evacuation distance and location of the inlet and exit. If people are in zones with a risk level of 4 or 5, a rapid evacuation is necessary for preventing human casualties. The proposed methodology incorporated with the inundation model can be applied to any underground space regardless of the location of stairs, the number of exits, shape of rooms, or layout of the floor. Consequently, it will contribute to mitigating flood damage in an underground space.

Hazards threatening underground transport systems

“…Connection linking flood events on the surface with vulnerability to flooding of underground subway infrastructure. (Lyu et al 2016) b. Frameworks based on decision-making methods as networks theory and analytic hierarchy process for assessing the flood evolution process and consequences in underground spaces (Lyu et al 2018;Wu et al 2018a) c. Integration of a stormwater management model into a geographical information system to evaluate the flood risk in a specific metro system (Herath and Dutta 2004;Li et al 2018a;Lyu et al 2019a) d. Methodologies to obtaining risk level studying both flood intensity and evacuation difficulty in underground spaces like metro stations (Han et al 2019) e. Analysis of metro systems resilience in the face of flood hazards, studying the components failure steps (Gonzva et al 2017) f. Risk assessment for metro systems flooding events based on regional flood risk evaluation methods (Lyu et al 2019b) g. Evaluation of the waterlogging risk of metro infrastructure caused by rainstorm in a specific Metro system (Quan et al 2011) h. Assessment of the risk in a specific metro system against fluvial flooding (Compton et al 2009) i. Evacuation of ridership from inundated underground space (Ishigaki et al 2008(Ishigaki et al , 2010 Table 3.…”

Flow Pattern and Escape Hazards of People from Flood Intrusion into the Staircase of Underground Spaces with Multiple Rest Platforms

“…For example, China's urban flooding disaster in 2021 caused 59.01 million people to be affected [11]. As underground spaces, such as subway stations, are relatively closed-off, with only a few entrances and exits connected to the outside world, when flooding occurs, underground spaces can easily be flooded and trapped accidents can occur [12][13][14][15]. In recent years, flooding has intruded into subway stations in several cities in China, leading to station shutdowns and even casualties.…”

Inundation Resilience Analysis of Metro-Network from a Complex System Perspective Using the Grid Hydrodynamic Model and FBWM Approach: A Case Study of Wuhan

“…Forero-Ortiz used Mike21 to simulate the evolution of surface water flow near Barcelona metro stations and quantify extreme rainfall weather's contribution to exacerbating metro accidents [46]. Previous studies have only focused on the intrusion characteristics and stepwise flow but have not studied the dynamic inundation process between subway stations [47][48][49][50][51].…”

“…In recent years, the indicator weights comparison methods could be divided into multi-criteria decision methods (MCDM) and machine learning (ML) techniques. MCDM methods that are used to compare the weights of the indicators, such as the analytic hierarchy process (AHP), entropy weight method, TOPSIS method (technique for order preference by similarity to an ideal solution), grey theory, and fuzzy methods [51][52][53][54][55][56]. The disadvantage of these methods, however, is that they rely excessively on the experience of experts to compare these indicators [57,58].…”