Intelligent Early Warning and Decision Platform for Long-Term Ground Subsidence in High-Density Areas for Sustainable Urban Development

Baoping Zou, Kejian Xia, Yansheng Deng (), Jundong Mu, Siqi Cheng and Chun Zhu
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Baoping Zou: School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China
Kejian Xia: School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China
Yansheng Deng: School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China
Jundong Mu: Hangzhou Metro Group Ltd., Hangzhou 310003, China
Siqi Cheng: General Supervision Station of Construction Engineering Quality and Safety of Hangzhou, Hangzhou 310005, China
Chun Zhu: School of Earth Sciences and Engineeing, Hohai University, Nanjing 210098, China

Sustainability, 2024, vol. 16, issue 7, 1-12

Abstract: Long-term ground subsidence (LTGS) is a relatively slow process. However, the accumulation of long-term subsidence has an adverse impact on the normal operation and safety of a subway, hindering sustainable urban development. A wide gap exists between early warning theory and its application in the control of LTGS during subway operation due to time span limitation. Providing decision support for LTGS in high-density urban areas during subway operation is difficult, and a collaborative decision system for real-time early warning and intelligent control is currently lacking. This study establishes the functional components of an intelligent early warning and decision platform, proposes a software system module, constructs an overall software framework structure, and develops a mobile intelligent early warning and decision platform. Moreover, this study introduces an early warning method for LTGS in high-density urban areas during subway operation. This method integrates an intelligent early warning decision-making platform, namely Differential Synthetic Aperture Radar Interferometry (DInSAR), land subsidence monitoring, operation tunnel subsidence monitoring, and other multisource data coupling. The method is applied to sections of the Hangzhou Metro Line 4 Phase I Project (Chengxing Road Station (CRS)–Civic Center Station (CCS)–Jiangjin Road Station (JRS) and Xinfeng Station (XS)–East Railway Station (ERS)–Pengbu Station (PS)). This work can serve as a reference for ensuring urban safety and promoting sustainable development.

Keywords: LTGS; intelligent warning; high-density urban areas; decision platform; sustainable urban development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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