Intelligent Risk Prognosis and Control of Foundation Pit Excavation Based on Digital Twin

被引:10
|
作者
Sun, Zhe [1 ,2 ]
Li, Haoyang [3 ]
Bao, Yan [1 ,2 ]
Meng, Xiaolin [1 ,2 ]
Zhang, Dongliang [1 ,2 ]
机构
[1] Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China
[2] Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China
[3] China Univ Min & Technol Beijing, Sch Mech & Civil Engn, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
digital twin; foundation pit excavation; risk prognosis and control; construction safety; INDEX;
D O I
10.3390/buildings13010247
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Timely risk information acquisition and diagnosis during foundation pit excavation (FPE) processes are vital for ensuring the safe and effective construction of underground urban infrastructures. Unfortunately, diverse geological and hydrogeological conditions and complex shapes of the foundation pit create barriers for reliable FPE risk prognosis and control. Furthermore, typical support systems during FPE use temporary measures, which have limited capacity to confront excessive loads, large deformations, and seepage. This study aims to establish an intelligent risk prognosis and control framework based on digital twin (DT) for ensuring safe and effective FPE processes. Previous studies have conducted extensive experimental and numerical analyses for examining unsafe conditions during FPE. How to enable intelligent risk prognosis and control of tedious FPE processes by integrating physics-based models and sensory data collected in the field is still challenging. DT could help to establish the interaction and feedback mechanisms between the physical and virtual space. In this study, the authors have established a DT model that consists of a physical space model and a high-fidelity physics-based model of a foundation pit in virtual space. As a result, a mechanism for effective acquisition and fusion of heterogeneous information from both physical and virtual space is established. Then, the authors proposed an integrated model and data-driven approach for examining safety risks during FPE. In the end, the authors have validated the proposed method through a case study of the FPE of the Wuhan Metro Line. The results show that the proposed method could provide theoretical and practical support for future intelligent FPE.
引用
收藏
页数:31
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