Proceedings of the 2023 5th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2023)

Prediction method of slope construction monitoring indicators based on LMD-SSA-BP

Authors
Yanru Jin1, *, Pingjie Li1, Mingjie Chen1
1China CCCC Fourth Harbor Engineering Institute Co., Ltd., Guangzhou, Guangdong, 510000, China
*Corresponding author. Email: 769474265@qq.com
Corresponding Author
Yanru Jin
Available Online 24 April 2024.
DOI
10.2991/978-94-6463-398-6_7How to use a DOI?
Keywords
SSA; BP; Slope Engineering; construction monitoring; LMD
Abstract

During the construction process of slope engineering, it is greatly affected by the environment and has many safety risks and hidden dangers. By monitoring data and predicting values based on historical monitoring data, dynamic real-time monitoring of ship lock engineering is carried out to determine and adjust future construction processes and safety control measures. This study constructed a computational model (SSA-BP model) for monitoring, forecasting and evaluation of slope engineering during construction period based on machine learning methods. The model is based on the relationship between monitoring data and time, and LMD is introduced for filtering calculation to clarify the trend of monitoring indicators; The sparrow search algorithm was introduced to optimize the model parameters of the BP neural network, making the prediction effect of the model more ideal. After comparative analysis, it was found that LMD has a good filtering effect, and the error evaluation results of the SSA-BP model are better than the BP model and closer to the true values. Research has shown that the prediction effect of LMD-SSA-BP model is closer to the actual engineering monitoring results, and has certain guiding significance and auxiliary decision-making role for future construction.

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 2023 5th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2023)
Series
Atlantis Highlights in Engineering
Publication Date
24 April 2024
ISBN
10.2991/978-94-6463-398-6_7
ISSN
2589-4943
DOI
10.2991/978-94-6463-398-6_7How 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

TY  - CONF
AU  - Yanru Jin
AU  - Pingjie Li
AU  - Mingjie Chen
PY  - 2024
DA  - 2024/04/24
TI  - Prediction method of slope construction monitoring indicators based on LMD-SSA-BP
BT  - Proceedings of the 2023 5th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2023)
PB  - Atlantis Press
SP  - 52
EP  - 62
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-398-6_7
DO  - 10.2991/978-94-6463-398-6_7
ID  - Jin2024
ER  -