An Integrated Intelligent Approach for Monitoring and Management of a Deep Foundation Pit in a Subway Station

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 22; p. 8737
• Main Authors: Hong, Chengyu, Zhang, Jinyang, Chen, Weibin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.11.2022; MDPI
• Subjects: Accuracy; Algorithms; Analysis; Application programming interface; Artificial intelligence; Automation; Building information modeling; Building management systems; Case studies; Civil engineering; Construction; Deep foundations; digital twin; Digital twins; Drone aircraft; Foundation construction; Frequency analysis; Frequency domain analysis; Geological surveys; Geology; intelligent foundation pit; internet of things; Keywords; Light rail transit; Machine learning; Machine vision; Maintenance; Microelectromechanical systems; Monitoring; Neural networks; Optical frequency; Railroads; Safety Management; Sensors; Shear tests; Software; Subway construction; Subway stations; Subways; transparent geology; Unmanned aerial vehicles; China; United States > US; digital twin; transparent geology; intelligent foundation pit; internet of things; machine learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22228737

As the scale of foundation pit projects of subway stations in Shenzhen becomes larger, and the construction constraints become more and more complex, there is an urgent need for intelligent monitoring and safety management of foundation pits. In this study, an integrated intelligent approach for monitoring and management of a deep foundation pit in a subway station was proposed and a case study based on the Waterlands Resort East Station Project of Shenzhen Metro Line 12 was used for validation. The present study first proposed the path of intelligent foundation pit engineering. Based on geotechnical survey and building information modeling, a three-dimensional transparent geological model of foundation pit was constructed. Multi-source sensing technologies were integrated, including micro electromechanical system sensing technology, Brillouin optical frequency domain analysis sensing technology, an unmanned aerial vehicle and machine vision for real-time high-precision wireless monitoring of the foundation pit. Moreover, machine learning models were developed for predicting key parameters of foundation pits. Finally, a digital twin integrated platform was developed for the management of the subway foundation pit in both construction and maintenance phases. This typical case study is expected to improve the construction, maintenance and management level of foundation pits in subway stations.