Urban flooding alert: Subway tunnels get early warning system

In recent years, urban waterlogging disasters have become more frequent due to rapid urbanization and climate change, severely threatening city infrastructure. Subway tunnels, with their semi-enclosed structure, face significant risks during floods, leading to difficult evacuations and substantial casualties. Statistics show that over 160 cities in China experience flood disasters annually, causing severe economic losses and fatalities. Due to these challenges, in-depth research on flood monitoring and early warning systems for subway tunnels is essential to enhance urban disaster prevention and mitigation capabilities.

Researchers from Tsinghua University, in collaboration with Beijing Urban Construction Design & Development Group Co., Ltd., have developed an innovative monitoring system. Published in the Journal of Intelligent Construction on May 16, 2024, the study (DOI: 10.26599/JIC.2024.9180011) presents a comprehensive approach to flood inundation depth monitoring and prediction in subway tunnels.

The study introduces an intelligent system designed for real-time monitoring and early warning of flood invasions in subway tunnels. Through scaled model experiments, the researchers identified fundamental flood patterns and developed formulas to predict water depth and flow dynamics. This system can quickly determine the flow rate and entry point of floodwater, forecasting future flood trends at a relatively low cost. The workflow is divided into three phases: pre-disaster, disaster outbreak, and during-disaster, each with specific monitoring requirements. This comprehensive approach allows the system to provide accurate and timely information for emergency decision-making. By integrating meteorological and hydrological data with real-time tunnel monitoring, the system enhances the ability to track flood risks and respond effectively to emergencies, offering a practical tool for improving subway safety during urban waterlogging events.

Dr. Hong Huang, the lead researcher, stated, "Our system represents a significant advancement in urban safety technology. By integrating real-time data and predictive modeling, we can offer immediate and actionable insights during flood emergencies, potentially saving lives and reducing economic losses."

This innovative flood monitoring and early warning system has broad applications in enhancing subway safety and urban infrastructure resilience. It can be integrated into smart city frameworks, providing critical data for disaster management and emergency response teams. The system's predictive capabilities offer a proactive approach to flood disaster mitigation, ensuring safer and more resilient urban environments.

This work was supported by the National Natural Science Foundation of China (No. 72091512) and Department of Engineering Physics, Tsinghua University–Beijing Urban Construction Design & Development Group Co., Limited Joint Research Center for Urban Disaster Prevention and Safety.

About Journal of Intelligent Construction

Journal of Intelligent Construction (JIC), sponsored by Tsinghua University and the China National Committee on Large Dams, published by Tsinghua University Press (TUP) and exclusively available via SciOpen, is a peer-reviewed journal for publishing original research papers, case studies, reviews and comments regarding the use of novel technologies in all domains of civil engineering, e.g., hydraulic engineering, structural engineering, geotechnical engineering, transportation, and construction management. The journal focuses on the application of advanced theories, methodologies, and tools, such as machine learning, sensors, robotics, 5G, the Internet of Things, artificial intelligence, building information modelling, and computational methods, etc., in all stages of the construction life cycle, which makes the process more intelligent and efficient. The journal also covers other essential areas of civil engineering, e.g., planning and design, operation and maintenance, and disaster mitigation.

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