Ground Settlement Prediction by Vacuum Preloading Based on LSTM

doi:10.16183/j.cnki.jsjtu.2023.340

Abstract

Abstract:

In order to explore a more accurate method for predicting settlement in vacuum preloading foundation treatment, a vacuum preloading settlement prediction model based on long short-term memory (LSTM) neural network was developed, taking the second-phase land reclamation project in the East Park of Xiamen New Airport planning area as an example. Measured settlement data from two regions were selected as the dataset, and the results were compared with traditional settlement prediction methods including the Asaoka method, three-point method, and hyperbolic method. The results show that the prediction model based on the LSTM neural network considering only sedimentation time series outerperforms the traditional methods that rely only on sedimentation time series. When the vacuum film is damaged and settlement rebound occurs under vacuum precompression foundation treatment, the root mean squared error (e_RMSE) and the mean absolute error (e_MAE) of LSTM model decrease by more than 45% compared to the traditional methods. Additionly, this model accurately captures the settlement rebound trend, providing more reliable prediction. In terms of prediction error, the e_RMSE and e_MAE values of the LSTM model which considers vacuum degree and sedimentation are lower than those of the LSTM model which only considers sedimentation time series by over 60%. This paper offers an advanced data-driven prediction method for prediction in vacuum preloading foundation settlement.

Key words: deep learning, long short term memory (LSTM), vacuum preloading, settlement prediction

CLC Number:

LIANG Yuwan, XIAO Zhaoyun, LI Mingguang, MENG Jiangshan, ZHOU Jianfeng, HUANG Shanjing, ZHU Haojie. Ground Settlement Prediction by Vacuum Preloading Based on LSTM[J]. Journal of Shanghai Jiao Tong University, 2025, 59(4): 525-532.

Figures/Tables 10

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References 13

[1]	SRIDHARAN A, MURTHY N S, PRAKASH K. Rectangular hyperbola method of consolidation analysis[J]. Géotechnique, 1987, 37(3): 355-368.
[2]	曾国熙, 杨锡令. 砂井地基沉陷分析[J]. 浙江大学学报, 1959(3): 34-72.
	ZENG Guoxi, YANG Xiling. Analysis of sand well foundation subsidence[J]. Journal of Zhejiang University, 1959(3): 34-72.
[3]	ASAOKA A. Observational procedure of settlement prediction[J]. Soils and Foundations, 1978, 18(4): 87-101.
[4]	潘林有, 谢新宇. 用曲线拟合的方法预测软土地基沉降[J]. 岩土力学, 2004, 25(7): 1053-1058.
	PAN Linyou, XIE Xinyu. Observational settlement prediction by curves fitting methods[J]. Rock and Soil Mechanics, 2004, 25(7): 1053-1058.
[5]	黄广军. Asaoka法预测软土地基沉降时存在的问题和对策[J]. 岩土力学, 2016, 37(4): 1061-1065.
	HUANG Guangjun. Problems and their solutions in predicting soft ground settlement based on Asaoka’s method[J]. Rock Soil Mechanics, 2016, 37(4): 1061-1065.
[6]	HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. doi: 10.1162/neco.1997.9.8.1735 pmid: 9377276
[7]	XU Y, LU X, CETINER B, et al. Real-time regional seismic damage assessment framework based on long short-term memory neural network[J]. Computer-Aided Civil and Infrastructure Engineering, 2021, 36(4): 504-521.
[8]	SENANAYAKE S, PRADHAN B, ALAMRI A, et al. A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction[J]. Science of The Total Environment, 2022, 845: 157-220.
[9]	刘俊城, 谭勇, 张生杰. 地铁车站深基坑开挖变形智能多步预测方法[J]. 上海交通大学学报, 2024, 58(7): 1108-1117. doi: 10.16183/j.cnki.jsjtu.2022.419
	LIU Juncheng, TAN Yong, ZHANG Shengjie. Intelligent multi-step prediction method for deep foundation pit deformation of subway station[J]. Journal of Shanghai Jiao Tong University, 2024, 58(7): 1108-1117.
[10]	GAO B, WANG R R, LIN C, et al. TBM penetration rate prediction based on the long short-term memory neural network[J]. Underground Space, 2021, 6(6): 718-731.
[11]	HU Y, GU C, MENG Z, et al. Prediction for the settlement of concrete face rockfill dams using optimized LSTM model via correlated monitoring data[J]. Water, 2022, 14(14): 21-57.
[12]	王启贵, 吴忠明, 陈兴红. 基于机器学习LSTM方法的大面积高填土沉降预测[J]. 工程勘察, 2022, 50(8): 41-45.
	WANG Qigui, WU Zhongming, CHEN Xinghong. Large area high fill settlement prediction based on machine learning LSTM method[J]. Engineering Investigation and Investigation, 2022, 50(8): 41-45.
[13]	龚晓南, 岑仰润. 真空预压加固软土地基机理探讨[J]. 哈尔滨建筑大学学报, 2002(2): 7-10.
	GONG Xiaonan, CEN Yangrun. Discussion on mechanism of vacuum preloading strengthening soft soil foundation[J]. Journal of Harbin Jianzhu University, 2002(2): 7-10.

阶段	LSTM		三点法		双曲线法		浅岗法
阶段	e_RMSE/mm	e_MAE/mm	e_RMSE/mm	e_MAE/mm	e_RMSE/mm	e_MAE/mm	e_RMSE/mm	e_MAE/mm
拟合阶段	3.5	3.0	3.3	2.2	10.7	9.0	1.3	0.4
预测阶段	0.7	0.6	1.0	0.9	18.1	14.7	1.5	1.3

阶段	LSTM		三点法		双曲线法		浅岗法
阶段	e_RMSE/mm	e_MAE/mm	e_RMSE/mm	e_MAE/mm	e_RMSE/mm	e_MAE/mm	e_RMSE/mm	e_MAE/mm
拟合阶段	3.9	3.4	2.4	1.5	2.5	2.1	3.5	2.9
预测阶段	2.7	2.3	12.4	10.7	13.6	11.5	5.0	4.3

区域	只考虑沉降时序		考虑真空度和沉降时序
区域	e_RMSE/ mm	e_MAE/ mm	e_RMSE/ mm	e_MAE/ mm
B6-1-17	0.7	0.6	0.4	0.4
B4-1-6	2.7	2.3	0.6	0.9
B4-1-6(沉降回弹阶段)	3.4	3.1	1.1	0.8

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