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ISSN 3080-8022(Print)
ISSN 3080-8030(Online)
CODEN:SOABCV
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Zheng Wentao
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Xiaoming Li,72nd Group Army Hospital,People’s Liberation Army of China/Min Liu,Affiliated Hospital of Gansu University of Chinese Medicine,China/Xiabing Chen,China/Bishan Chen,China/Miaona Li,China/Shengmei Ye,China/Yili Zhang,China/Xiaomin Zhao,China/Dawen Peng,China/Jieping Xu,China / Suleman Khan,UK/Saraju P.Mohanty,USA/Gautam Srivastava,Canada(The above rankings are not in order)
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Shubin LiuPhD/Gansu Provincial Hospital of Traditional Chinese Medicine,China
Yuwei Zhang/Chongqing University,China
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Xiaojing Li,China
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《Sci Online》( ISSN 3080-8022、EISSN 3080-8030 ) Publisher:Quest Press Release Date:2025/10/9
10.12479/questpress-scionline.20250103 Open Access Downloaded 3 Viewed 35

 

Unraveling the Power of IoT and Big Data in Urban Rail Transit Tunnel Intelligent Safety Monitoring

Hongjie Xu   Lixin Li   Jiakai Han
TIANJIN DONGFANG Tairui TECHNOLOGY CO.,LTD, Tianjin 300171, China
Abstract:With the global explosive growth of urban rail transit, urban rail transit tunnels (core transit arteries) have become more complex and lengthy, facing rising safety risks from natural factors, mechanical wear, and corrosion. Traditional monitoring methods (manual inspections, basic fixed-point sensors) are inefficient, narrowly covered, and low in data accuracy, failing to meet modern safety needs. This study develops an intelligent safety monitoring system for such tunnels using IoT and Big Data. It designs core components: IoT sensor networks, Big Data management, and machine learning-based safety assessment algorithms. A Shanghai case study shows the system detected minor structural displacements (0.5–1.5 mm) and abnormal gas concentrations, cutting unplanned disruptions by ~30%. It also establishes evaluation indicators and proposes optimizations (5G, edge computing), addressing traditional monitoring shortcomings.
Keywords:Urban Rail Transit Tunnel; Intelligent Safety Monitoring; Internet of Things (IoT); Big Data; Anomaly Detection
 
 
 
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