基于LSTM算法的基坑变形预测

张生杰; 谭勇

doi:10.3973/j.issn.2096-4498.2022.01.014

隧道建设 >

2022 , Vol. 42 >Issue 1: 113 - 120

DOI: https://doi.org/10.3973/j.issn.2096-4498.2022.01.014

研究与探索

基于LSTM算法的基坑变形预测

张生杰 ,
谭勇

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(同济大学地下建筑与工程系，上海 200092)

张生杰(1997—)，男，山东临沂人，同济大学建筑与土木工程专业在读硕士，研究方向为基坑变形预测。E-mail: 1932452@tongji.edu.cn。*通信作者：谭勇， E-mail: tanyong21th@tongji.edu.cn。

网络出版日期: 2022-01-28

基金资助

国家自然科学基金(41877286)

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Deformation Prediction of Foundation Pit Based on Long Short-Term Memory Algorithm

ZHANG Shengjie ,
TAN Yong

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(Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China)

Online published: 2022-01-28

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摘要

在基坑正常施工的情况下，为准确预测基坑未来一段时间内地下连续墙的水平变形，基于某地铁基坑工程现场监测数据，利用LSTM算法进行模型训练，得到基坑变形预测模型。在前期实测数据的基础上，利用预测模型分别对基坑开挖期、浇筑底板后2种工况下的基坑地下连续墙水平变形进行预测，得到基坑地下连续墙的变形预测值，并结合其他预测模型的预测结果进行误差对比分析。结果表明：相比于BP预测模型和灰色预测模型，LSTM预测模型具有更高的准确性。通过对多测点多工况的进一步预测验证，证明了该模型的稳定性和可靠性。

关键词： 基坑工程; 变形预测; 监测数据; LSTM

本文引用格式

张生杰 , 谭勇 . 基于LSTM算法的基坑变形预测[J]. 隧道建设, 2022 , 42(1) : 113 -120 . DOI: 10.3973/j.issn.2096-4498.2022.01.014

Abstract

The longshort-term memory(LSTM) algorithm is used for model training based on the field monitoring data of a metro foundation pit to accurately predict the horizontal deformation of diaphragm walls of foundation pits. The model predicts the horizontal deformation of the diaphragm wall of the foundation pit under two conditions: excavation and postfoundation slab-pouring, and the predicted results are compared with those obtained using other models. The results show that the prediction accuracy of LSTM model is higher compared with the BP prediction and gray prediction models. The additional prediction based on multiple measuring points and working conditions indicates the recommended model′s stability and reliability.

Key words： foundation pit; deformation prediction; monitoring data; longshort-term memory algorithm

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