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Received:January 20, 2021 Revised:February 23, 2021
Received:January 20, 2021 Revised:February 23, 2021
中文摘要: 伴随着社会经济的快速发展, 地铁、隧道、桥梁等建筑在人们的生活中占据的地位越来越高, 预测分析建筑的结构变形数据, 及时发现存在的安全隐患, 至关重要. 结合长短时记忆网络(Long Short Time Memory, LSTM)的优点, 本文提出了一种基于双向长短时记忆网络(Bidirectional Long Short Time Memory, Bi-LSTM)的结构变形预测模型. 该模型通过记忆时间节点前后的规律, 预测当前节点变形数据, 充分挖掘变形数据内部的关联信息. 与WNN、LSTM、GRU模型进行对比, 结果表明, 该模型RMSE、MAPE、MAE分别下降了66.0%、61.2%、66.2%, 是一种有效预测结构形变的方法.
Abstract:With the rapid development of social economy, subways, tunnels, and bridges are occupying higher positions in people’s lives. Predicting and analyzing the structural deformation data of buildings and discovering hidden safety hazards in time have become indispensable for structural safety monitoring. Combining the advantages of Long Short Time Memory (LSTM), this study proposes a structural deformation prediction model based on Bidirectional Long Short Time Memory (Bi-LSTM). The model predicts the deformation data of the current node by memorizing the rules before and after the time node and fully mines the relevant information within the deformation data. Compared with WNN, LSTM, and GRU models, this model, with RMSE, MAPE, and MAE reduced by 66.0%, 61.2%, and 66.2% respectively, proves to be an effective method for predicting structural deformation.
keywords: structural deformation prediction LSTM Bi-LSTM WNN GRU
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基金项目:国家重点研发计划(2018YFC0808706); 中铁一院科研项目(19-42-02)
引用文本：
王亚飞,韩静,郭凰,廖聪,王立新.基于Bi-LSTM的结构变形预测.计算机系统应用,2021,30(11):304-309
WANG Ya-Fei,HAN Jing,GUO Huang,LIAO Cong,WANG Li-Xin.Prediction of Structural Deformation Based on Bi-LSTM.COMPUTER SYSTEMS APPLICATIONS,2021,30(11):304-309
王亚飞,韩静,郭凰,廖聪,王立新.基于Bi-LSTM的结构变形预测.计算机系统应用,2021,30(11):304-309
WANG Ya-Fei,HAN Jing,GUO Huang,LIAO Cong,WANG Li-Xin.Prediction of Structural Deformation Based on Bi-LSTM.COMPUTER SYSTEMS APPLICATIONS,2021,30(11):304-309
