基于双向门控式宽度学习系统的监测数据结构变形预测
Structural deformation prediction of monitoring data based on bi-directional gate board learning system
罗向龙 1王亚飞 2王彦博 1王立新3
作者信息
- 1. 长安大学 信息工程学院,陕西 西安 710064
- 2. 中兴通讯股份有限公司,陕西 西安 710111
- 3. 中铁第一勘察设计院集团有限公司,陕西 西安 710043;西安理工大学 土木建筑工程学院,陕西 西安 710048
- 折叠
摘要
监测数据深度学习预测模型运算量大、实时性差,为此结合宽度学习系统(BLS)和双向长短时记忆(Bi-LSTM)模型的优势,提出基于双向门控式宽度学习系统(Bi-G-BLS)的结构变形预测模型.对BLS的特征节点增加循环反馈和遗忘门结构,提高当前节点对前一节点的依赖关系,分别从正向和反向提取时间序列的内部特征,充分挖掘数据的双向特征,在提高模型预测精确度的同时减少模型预测时间.基于实测的地铁基坑沉降监测数据的测试结果显示,所提预测模型与门控循环单元(GRU)、BLS、Bi-LSTM、G-BLS模型相比,均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)平均分别降低了 21.04%、12.81%、24.41%;在预测精度相近的情况下,所提模型的预测时间比Bi-LSTM模型降低了 99.59%.结果表明,所提模型在预测速度和精确度上较对比模型有明显提升.
Abstract
Aiming at the shortcomings of large computation load and poor real-time performance for deep learning models with monitoring data,combining the advantages of board learning system(BLS)and bi-directional long short term memory(Bi-LSTM)model,a structural deformation prediction model was proposed based on bi-directional gate board learning system(Bi-G-BLS).The cyclic feedback structure and the forget-gate structure were added to the feature nodes of BLS to improve the dependence of the current node on the previous node,and the internal features of the time series were extracted from the forward and reverse respectively to make full use of the bidirectional data characteristics.As a result,the prediction accuracy was improved effectively while the prediction time was greatly reduced.The test results of the measured monitoring data for the subway foundation pit settlement showed that compared with the gated recurrent unit(GRU),BLS,Bi-LSTM,and G-BLS models,the root mean square error(RMSE),mean absolute error(MAE)and mean absolute percentage error(MAPE)of the proposed model were reduced by 21.04%,12.81%and 24.41%.The prediction time of the proposed model was reduced by 99.59%compared to Bi-LSTM model with similar prediction accuracy.The results showed that the prediction speed and accuracy of the proposed model were significantly improved over the comparison models.
关键词
结构变形/预测模型/深度学习/门控循环单元(GRU)/宽度学习系统(BLS)Key words
structural deformation/predictive model/deep learning/gated recurrent unit(GRU)/board learning system(BLS)引用本文复制引用
基金项目
国家重点研发计划(2018YFC0808706)
国家自然科学基金(52078421)
陕西省"特支计划"青年拔尖人才项目(陕组通字[2018]33号)
中铁一院科研项目(院科 19-40)
出版年
2024
NETL
NSTL
万方数据