Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)

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Intelligent Foundation Pit based on Digital Twin Technology Safety Monitoring and Prediction

Authors
Jingxian Sun1, *, Cheng Zhang1, Shen Luo1, Fei Meng1, Jian Gao1, Jinyuan Wu1, Chaowen Xie1, Tianheng Guo1
1Guangdong Power Grid Limited Liability Company Zhongshan Power Supply Bureau, Zhongshan, China
*Corresponding author. Email: 531950053@qq.com
Corresponding Author
Jingxian Sun
Available Online 31 August 2024.
DOI
10.2991/978-94-6463-490-7_10How to use a DOI?
Keywords
Foundation pit; Deformation monitoring; BIM; Deep learning
Abstract

With the increasing scale of foundation pit engineering, construction constraints are becoming more and more complex, under this background, it is urgent to carry out automatic monitoring and safety control of foundation pit Taking Tagang Village foundation pit project in Zengcheng District of Guangzhou as an example, a new automatic safety monitoring, prediction and early warning technology for the whole life of foundation pit is proposed, which provides a new idea for the stability evaluation of foundation pit. This technology combines tilt photography technology, BIM and deep learning to develop a digital twin integrated platform for foundation pit construction and maintenance stage management, which successfully realizes comprehensive, accurate and real-time monitoring and prediction of foundation pit, and provides more reliable and detailed data support for evaluating the stability of foundation pit. This not only improves the safety and reliability of foundation pit engineering, but also shows significant application value in the engineering field.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 August 2024
ISBN
978-94-6463-490-7
ISSN
2589-4919
DOI
10.2991/978-94-6463-490-7_10How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jingxian Sun
AU  - Cheng Zhang
AU  - Shen Luo
AU  - Fei Meng
AU  - Jian Gao
AU  - Jinyuan Wu
AU  - Chaowen Xie
AU  - Tianheng Guo
PY  - 2024
DA  - 2024/08/31
TI  - Intelligent Foundation Pit based on Digital Twin Technology Safety Monitoring and Prediction
BT  - Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
PB  - Atlantis Press
SP  - 75
EP  - 81
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-490-7_10
DO  - 10.2991/978-94-6463-490-7_10
ID  - Sun2024
ER  -