基于长短时记忆网络的深基坑变形安全风险预警

夏天; 成诚; 庞奇志

doi:10.3799/dqkx.2021.250

Volume 48 Issue 10

Oct. 2023

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2023 > 48(10): 3925-3931

Xia Tian, Cheng Cheng, Pang Qizhi, 2023. Safety Risk Warning of Deep Foundation Pit Deformation Based on LSTM. Earth Science, 48(10): 3925-3931. doi: 10.3799/dqkx.2021.250

Citation:

Xia Tian, Cheng Cheng, Pang Qizhi, 2023. Safety Risk Warning of Deep Foundation Pit Deformation Based on LSTM. Earth Science, 48(10): 3925-3931. doi: 10.3799/dqkx.2021.250

Xia Tian, Cheng Cheng, Pang Qizhi, 2023. Safety Risk Warning of Deep Foundation Pit Deformation Based on LSTM. Earth Science, 48(10): 3925-3931. doi: 10.3799/dqkx.2021.250

Citation:

Xia Tian, Cheng Cheng, Pang Qizhi, 2023. Safety Risk Warning of Deep Foundation Pit Deformation Based on LSTM. Earth Science, 48(10): 3925-3931. doi: 10.3799/dqkx.2021.250

PDF( 1656 KB)

Safety Risk Warning of Deep Foundation Pit Deformation Based on LSTM

doi: 10.3799/dqkx.2021.250

Faculty of Engineering, China University of Geosciences, Wuhan 430074, China

Received Date: 2021-11-05
Available Online: 2023-10-31
Publish Date: 2023-10-25

Abstract

Abstract

In order to prevent deep foundation pit construction safety accidents, a set of risk warning standards based on monitoring data is proposed, and a deep foundation pit deformation safety risk warning model based on long short-term memory (LSTM) was established. Relying on the actual deep foundation pit engineering project, the risk warning model is applied to it to make short-term predictions of the deformation of each monitoring item of the foundation pit. The maximum error between the predicted data and the actual data is 5.04%, the minimum error is 0.04%, and the average relative error is 2.41%, which proves that the prediction effect of the model is good. It shows that the LSTM-based deep foundation pit deformation safety risk early warning model has good accuracy and superiority in the prediction of foundation pit deformation, and can provide a reliable guarantee for the safety judgment and risk management of foundation pit engineering.
- deep foundation pit,
- foundation pit safety,
- deformation prediction,
- long-term and short-term memory,
- risk warning,
- safty engineering,
- engineering geology

FullText(HTML)

References(18)

References

Chen, Y. R., 2018. Application of Intelligent Algorithm Based on Genetic Algorithm and Extreme Learning Machine to Deformation Prediction of Foundation Pit. Tunnel Construction, 38(6): 941-947(in Chinese with English abstract).

Ghaboussi, J., Sidarta, D. E., 1997. New Method of Material Modeling Using Neural Networks. 6th International Symposium On Numerical Models in Geotechnics, 393-400.

Hashash, Y. M. A., Whittle, A. J., 1996. Ground Movement Prediction for Deep Excavations in Soft Clay. Journal of Geotechnical Engineering, 122(6): 474-486. https://doi.org/10.1061/(asce)0733-9410(1996)122:6(474)

Li, Q., 2013. Application Research of Generalized Regression Neural Network in Deep Foundation Pit Deformation Monitoring (Dissertation). Central South University, Changsha(in Chinese with English abstract).

Li, Y. J., Xue, Y. D., Yue, L., et al., 2015. Displacement Prediction of Deep Foundation Pit Based on Genetic Algorithms and BP Neural Network. Chinese Journal of Underground Space and Engineering, 11(S2): 741-749(in Chinese with English abstract).

Ma, L., 2018. Study of Deformation Law of Deep Foundation Pit Based on Chaotic Progressive Prediction Model and Trend Test. Tunnel Construction, 38(6): 934-940(in Chinese with English abstract).

Peng, M., 2008. Research and Development of Dynamic Risk Management Software (TRM2.0) for Shield Tunnel Construction (Dissertation). Tongji University, Shanghai(in Chinese with English abstract).

Song, C. P., 2019. Improved BP Neural Network Method for Deformation Predication of Deep Excavation. Journal of Civil Engineering and Management, 36(5): 45-49, 55(in Chinese with English abstract).

van Staveren, M. T., Peters, T. J. M., 2004. Matching Monitoring, Risk Allocation and Geotechnical Baseline Reports. Engineering Geology for Infrastructure Planning in Europe. Springer, Berlin, Heidelberg : 786-791. https://doi.org/10.1007/978-3-540-39918-6_87

Wang, X., Wu, J., Liu, C., et al., 2018. Exploring LSTM Based Recurrent Neural Network for Failure Time Series Prediction. Journal of Beijing University of Aeronautics and Astronautics, 44(4): 772-784(in Chinese with English abstract).

Werbos, P. J., 1990. Backpropagation through Time: What It does and How to do It. Proceedings of the IEEE, 78(10): 1550-1560. https://doi.org/10.1109/5.58337

陈艳茹, 2018. 基于遗传算法和极限学习机的智能算法在基坑变形预测中的应用. 隧道建设(中英文), 38(6): 941-947. https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD201806012.htm

李钦, 2013. 泛化回归神经网络在深基坑变形监测中的应用研究(硕士学位论文). 长沙: 中南大学.

李彦杰, 薛亚东, 岳磊, 等, 2015. 基于遗传算法-BP神经网络的深基坑变形预测. 地下空间与工程学报, 11(增刊2): 741-749. https://www.cnki.com.cn/Article/CJFDTOTAL-BASE2015S2062.htm

马琳, 2018. 基于混沌递进预测模型与趋势检验的深基坑变形规律研究. 隧道建设(中英文), 38(6): 934-940. https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD201806010.htm

彭铭. 2008. 盾构隧道施工动态风险管理软件(TRM2.0)开发研究(硕士学位论文). 上海: 同济大学.

宋楚平, 2019. 一种改进的BP神经网络深基坑变形预测方法. 土木工程与管理学报, 36(5): 45-49, 55 https://www.cnki.com.cn/Article/CJFDTOTAL-WHCJ201905009.htm

王鑫, 吴际, 刘超, 等, 2018. 基于LSTM循环神经网络的故障时间序列预测. 北京航空航天大学学报, 44(4): 772-784. https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK201804015.htm

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(5) / Tables(6)

Get Citation

PDF

XML

Article views (557) PDF downloads(50)

Safety Risk Warning of Deep Foundation Pit Deformation Based on LSTM

doi: 10.3799/dqkx.2021.250

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content