Scenario inference model of urban metro system cascading failure under extreme rainfall conditions
Introduction
Recently, with China's urbanization, many metro lines have been constructed to reduce congestion [1], [2], [3]. Metro systems have been the lifelines of megacities, and hazards or incidents involving metro systems will cause severe problems in cities’ working and living conditions [4]. The metro systems are considered to be the most significant infrastructures of megacities. However, climate change has profound implications for the effectiveness and viability of critical infrastructures (especially metro systems), making the issue increasingly topical [5]. In 2021 alone, many metropolitan areas in China suffered severe metro flooding. For example, from 16:00 to 17:00, on July 20, the hourly rainfall was 201.9 mm and the passenger flow was 967. Floodwater caused by torrential rain encroached on the tunnel from Shakeoulu and Haitansi stations on Zhengzhou metro line 5. The flood in the metro reached people's necks, causing deaths and five injuries among the more than 500 trapped people [6]. The increasing damage due to extreme rainfall events has forced authorities in flood-prone cities to reevaluate their policy regarding the reliability of metro systems to future heavy rainfall events.
Cascading failure [7] is a kind of failure in a system comprising interconnected parts, in which a part's failure can trigger successive parts' failure. Due to the coupling relationships in complex networks, which are interconnected and continuously generate other nodes. Variations in any one node will affect other nodes potentially in the network; in particular, it will affect unexpected nodes that may amplify the damage of the accident. A well-known phenomenon of cascading is The Butterfly Effect. Cascading failure can be predicted by scenario-based methods, otherwise serious side effects can occur if unpredictable. To address the above issue, numerous scholars have studied the cascading failure of various critical infrastructures using the risk assessment methods, such as Bayesian networks (BNs) [8], [9], [10], Directed acyclic graph (DAG) [11], [12], [13], Petri Net (PN) [14], [15], [16], Markov process (MP) [17], [18], [19], etc. Fan et al. [20] proposed a method based on BNs, MP and deep reinforcement learning (DRL) to improve the reliability of supply in natural gas pipeline networks. For a similar problem of pipeline networks [21], the reliability of pipeline networks is optimized by a novel Manifold-based Conditional Bayesian network model. Liu et al. [22] explored the integration of triangular membership and regional center method to SPN for the modeling and evaluation of gas leakage emergency rescue process in gas transmission station. As the traditional critical infrastructure, the hospital is a crucial social component, especially during the spread of the Coronavirus (COVID-19) pandemic. Liu et al. [23] evaluated the seismic resilience of hospital buildings using BNs, considering the cascading relationships on nonstructural components. Silva et al. [24] proposed reliability and availability models adopting stochastic Petri net (SPN) to quantify the impact of energy resources and rejuvenate medical sensor networks' dependability. Similarly, the cascading failure mode of metro systems has been studied widely. Ye et al. [25] proposed a novel grey- Markov prediction model to describe different contribution degrees of historical information of future change trends of system characteristics. Li and Wu [26] proposed a deep reinforcement learning (RL)-based decision support system for stakeholders to optimally manage the critical components of transportation networks to minimize the network-level losses induced by hurricanes. Ghoushchi et al. [27] proposed a novel approach to select the optimal landfill for medical waste using Multi-Criteria Decision-Making (MCDM) methods. Wang et al. [28] applied the CIA-ISM approach to analyze emergency scenarios. Chen et al. [29] discussed the complex interrelationships among the barriers to building-integrated photovoltaics in Singapore. With the rapid development of technology, the emerging infrastructures, such as the Internet of Things (IoT) [30], cloud data centers [31], electric-cyber infrastructure [32], and computerization in supply chains [33], have aroused wild attention.
In this paper, the metro system is identified as a dynamic one impacted by extreme climate. The analysis of cascading process and the decoupling are complex in this case. Scenario-based methods can clearly describe the cascading failure and coupling mode, which can improve the decoupling efficiency and accuracy. In addition, system elements are difficult to quantify. Therefore, they are simplified into events. The system components involved in the cascading process are in the form of events, which reduce the computational dimension.
This study aims to address the research gaps inherent in the previous researches, which are summarized as follows:
1. Cascading failure analysis in the metro system has been conducted based on research with interaction coupling relationships and decoupling among system elements. With the development of extreme climate, the analysis of cascading failure mode becomes difficult in complex systems. Hence, how to properly assess the cross-impact relationships between essential events places an emerging challenge for the research into cascading failure.
2. Emergency measures are critical factors in reducing losses under extreme rainfall conditions. However, when investigating metro flooding risk, few studies have attempted to assess the efficiency of emergency management measures. Therefore, how to precisely analyze the priority and efficiency of emergency measures is the problem to be resolved in this study, which we believe is essential to the cascading failure research on metro systems.
3. In most studies, the CIA-ISM method's event probabilities are determined in two ways: assessed by historical case data and the Delphi method. The former CIA-ISM has accurate results but it is always hard to collect data, while the latter method is efficient in assessment but usually has violent subjectivity. Therefore, it is necessary to develop a scenario-based method that makes a trade-off between veracity and objectivity.
In this study, we focus on the metro systems under extreme rainfall conditions as the unit of analysis and regard excessive rainfall as the external perturbation of the system. Then based on the conventional CIA-ISM model, the ROC-based CIA-ISM model is proposed to infer the cascading failure scenario process in metro systems under extreme rainfall conditions, especially the cross-impact relationships between critical events. Finally, several representative scenario graphs are built to predict the influence of the specific events on the outcome events1. Furthermore, we adopt the BN model to demonstrate the efficiency of the ROC-based CIA-ISM. The findings can provide suggestions for metro management and emergency repair strategies for the metro system department under extreme rainfall conditions.
Our main contributions to this paper are:
We adopt the ROC method that was originally developed in multicriteria decision analysis (MCDA) for the elicitation of criteria weight, which turns ordinal judgments into ratio-scale information [[34], [35], [36]]. This type of elicitation only requires experts to provide a ranking of events according to their likelihood, providing a fast and nonnumerical elicitation process. Probabilities are subsequently approximated from the ranking by an algorithm based on the principle of maximum entropy. By using the ROC-based CIA-ISM method, we could fast obtain the initial probability of events. In addition, we could accurately assess the scenario inference process of cascading failure in complex systems and improve their evaluation efficiency.
The adoption of the ROC-based CIA-ISM approach that we proposed in this article was able to assess the reliability of critical emergency measures in metro systems under extreme rainfall conditions. We had predictably formalized its use for the ranking of emergency measures. In addition, we extended the prediction scale, so it could also estimate the efficiency of emergency management measures in complex systems comprehensively. Furthermore, we provided several special emergency measures, indicating their significance.
Using a simulation study, we tested the priority of emergency events under three outcome conditions. We compared the method to BN model and find that the proposed method is more accurate than the scenario-based method that we test. We took the ROC-based CIA-ISM approach in practice and adopted it to the “7.20” accident to get the cascading failure process, and gave some advices.
The rest of the paper is organized as follows: Section 2 describes the methodology we developed to perform the cascading failure analysis; Section 3 describes the test case used to demonstrate the applicability of the developed method; The analysis and results are presented in Section 4; Section 5 presents the conclusions.
Section snippets
Methodology
The ROC-based CIA-ISM model introduced in this paper comprises the Rank-order centroid method and the CIA-ISM model. Fig. 1 shows the collaborative modeling process based on the ROC-based CIA-ISM model.
Test case
Metro stations have strong closure, large passenger flow, complex passenger flow, and difficult emergency evacuation. Once an accident occurs, it quickly causes heavy casualties, induces secondary disasters or derivative disasters, and causes negative social impacts [42]. This paper intends to analyze the scenario evolution and system vulnerability nodes of metro flooding disasters given the cascading failure of metro systems in extreme rainfall. It takes the “7.20” metro flooding accident in
Comparison with other scenario-based methods
To further verify the reliability of the proposed method, we compared the cascading failure scenarios of the Bayesian network (BN) model and the ROC-based CIA-ISM. BN model, a scenario-based method, has been widely adopted for maritime accidents [44], blowout accidents [45], construction projects [46], and deep learning [47]. The ten cases [[2], [3], [4],6] are collected by using the BN model. The events and prior probabilities are given in Tables 2 and 3. Next, the conditional probabilities
Conclusions
Based on the analysis of the “7.20” metro flooding in Zhengzhou city, the events related to metro flooding are selected to form an event set. Most events relate to metro flooding, heavy rain secondary disaster, and system vulnerability. The initial probability of the event set can be obtained by the experts’ rank of ROC and probability elicitation. Experts in emergency management were invited to form a panel with first responders to provide consistent estimates of the causal relationship
CRediT authorship contribution statement
Zhen Yang: Conceptualization, Methodology. Xiaobin Dong: Investigation, Data curation, Writing – original draft. Li Guo: Supervision, Writing – review & editing.
Declaration of Competing Interest
We have no conflict of interest with anyone.
Zhen Yang is a professor at Xi 'an University of Architecture and Technology. His research interestsinclude safety big data and risk management, and AI-assisted safety system engineering. E-mail:[email protected]
References (52)
- et al.
Long-term settlement behaviour of metro tunnels in the soft deposits of Shanghai
Tunneling Undergr Space Technol
(2014) - et al.
Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach
Sci Total Environ
(2018) - et al.
Flood risk assessment of metro systems in a subsiding environment using the interval FAHP-FCA approach
Sustain Cities Soc
(2019) - et al.
Adapting critical infrastructure to climate change: a scoping review
Environ Sci Policy
(2022) - et al.
Chapter 9—cyberphysical security methods
Networked control systems
(2019) - et al.
Manifold-based conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements
Reliab Eng Syst Saf
(2022) - et al.
Modeling and performance analysis of gas leakage emergency disposal process in gas transmission station based on stochastic petri nets
Reliab Eng Syst Saf
(2022) - et al.
A probabilistic framework to evaluate seismic resilience of hospital buildings using Bayesian networks
Reliab Eng Syst Saf
(2022) - et al.
Model-driven impact quantification of energy resource redundancy and server rejuvenation on the dependability of medical sensor networks in smart hospitals
Sensors
(2022) - et al.
Deep reinforcement learning-based decision support system for transportation infrastructure management under hurricane events
Struct Saf
(2022)
Landfill site selection for medical waste using an integrated SWARA-WASPAS framework based on spherical fuzzy set
Sustainability
Teegraph: a blockchain consensus algorithm based on TEE and DAG for data sharing in IoT
J Syst Archit
A generalized petri net-based modeling framework for service reliability evaluation and management of cloud data centers
Reliab Eng Syst Saf
Cascading failures in interdependent infrastructures: an interdependent Markov-chain approach
IEEE Trans Smart Grid
Decision quality using ranked attribute weights
Manag Sci
Probability elicitation under severe time pressure: a rank-based method
Risk Anal
Collaborative scenario modeling in emergency management through cross-impact
Technol Forecast Soc Change
Risk mitigation strategies for critical infrastructures based on graph centrality analysis
Int J Crit Infrastruct Prot
A scenario-based model for earthquake emergency management effectiveness evaluation
Technol Forecast Soc Change
A framework for quantitative analysis of the causation of grounding accidents in arctic shipping
Reliab Eng Syst Saf
Application of integrated STAMP-BN in safety analysis of subsea blowout preventer
Ocean Eng
Using Bayesian networks for selecting risk-response strategies in construction projects
J Constr Eng Manag
Hard or soft flood adaptation? Advantages of a hybrid strategy for Shanghai
Glob Environ Chang
A review of advances in urban flood risk analysis over China
Stoch Environ Res Risk Assess
Random forests for classification in ecology
Ecology
Tornado hazards on June 23 in Jiangsu Province, China: preliminary investigation and analysis
Nat Hazards
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Zhen Yang is a professor at Xi 'an University of Architecture and Technology. His research interestsinclude safety big data and risk management, and AI-assisted safety system engineering. E-mail:[email protected]