基于Bayes和LSTM神经网络模型的基坑变形值预测研究

Research on Prediction of Deformation Value of the Foundation Pit Based On Bayesian and LSTM Neural Network Models

曹玉江¹

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作者信息

1. 中铁十八局集团市政工程有限公司,天津 300222
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摘要

为了提高基坑变形预测的准确性和可靠性,提出了一种基于贝叶斯方法(Bayes)和长短期记忆(Long Short-Term Memory,LSTM)神经网络的复合模型,并结合杭州市文一西路改造工程现场监测数据,比较了 Bayes-LSTM模型与其他预测模型在大跨度基坑上方的地表沉降与水平位移数据预测误差.研究结果表明:与LSTM模型和支持向量机(SVM)模型相比,Bayes-LSTM模型对基坑上方地表沉降的预测精度分别提高了 1.0和1.26,证明了 Bayes-LSTM模型在地表沉降预测方面表现出较高的预测精度和泛化能力.该研究成果可为大跨度基坑施工安全管理提供决策与支持.

Abstract

In order to improve the accuracy and reliability of pit deformation prediction,a composite model was pro-posed based on Bayesian method(Bayes)and long short-term memory(LSTM)neural network in this paper.Com-bined with the site monitoring data of Wenyi West Road renovation project,the prediction error of surface settlement and horizontal displacement data above the large span pit was compared by Bayes-LSTM model with other prediction models.The results show that the prediction accuracy has been improved by Bayes-LSTM model by 1.0 and 1.26 re-spectively compared with the LSTM model and the SVM(support vector machine)model,which is proved to have high prediction accuracy and generalization ability in the prediction of surface settlement.The study provides decision support for the safety management of large-span foundation pit construction.

关键词

基坑沉降/贝叶斯网络/LSTM神经网络/预测模型

Key words

foundation pit settlement/Bayesian network/long short-term memory(LSTM)neural network/predic-tive model

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出版年

2024

市政技术

中国市政工程协会　北京市政路桥股份有限公司　北京市政建设集团有限责任公司北京市市政工程研究院

市政技术

影响因子：0.385

ISSN：1009-7767

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