College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.
Underground Polis of Academy, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2022 Nov 11;22(22):8737. 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.
随着深圳市地铁站基坑工程规模的不断扩大，施工约束条件也越来越复杂，基坑的智能监测和安全管理变得尤为迫切。本研究提出了一种综合的地铁车站深基坑监测与管理的智能方法，并基于深圳地铁 12 号线水蓝之湾东站项目进行了案例验证。本研究首先提出了智能基坑工程的路径。基于岩土工程勘察和建筑信息建模，构建了基坑三维透明地质模型。集成了多种传感技术，包括微机电系统传感技术、布里渊光频域分析传感技术、无人机和机器视觉，实现了基坑的实时高精度无线监测。此外，还开发了机器学习模型，用于预测基坑的关键参数。最后，开发了一个数字孪生集成平台，用于地铁基坑在施工和维护阶段的管理。这个典型案例研究有望提高地铁站基坑的施工、维护和管理水平。
