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计算机系统应用:2020,29(9):212-218
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基于正交参数优化的LSTM结构变形预测模型
(长安大学 信息工程学院, 西安 710064)
Structure Deformation Prediction Model Based on LSTM and Orthogonal Parameter Optimization
(School of Information Engineering, Chang’an University, Xi’an 710064, China)
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投稿时间:2020-02-24    修订日期:2020-03-17
中文摘要: 随着社会经济的蓬勃发展,地铁、隧道、桥梁等大型建筑的需求也越来越大.通过对结构变形数据的分析与预测,可以判断结构未来的发展趋势,对安全隐患提前预警和采取应急措施,预防灾害的发生.由于变形监测数据通常具有不稳定性和非线性的特点,使得监测数据预测成为结构监测研究中的一个难题.针对结构变形预测模型存在的问题,本文提出了一种基于正交参数优化的长短时记忆网络(LSTM)结构变形预测模型.该模型通过LSTM网络结构获得时间序列的长期记忆,充分挖掘变形数据的内部时间特征;并通过正交试验对LSTM模型的参数进行优化;最后通过实测数据对模型进行验证,实验结果表明,模型预测值与实际监测值吻合较好.通过与WNN、DBN-SVR和GRU模型相比,平均RMSEMAEMAPE分别降低了56.01%、52.94%和52.78%,本文提出的基于正交参数优化的LSTM结构变形预测模型是一种有效的结构沉降方法,为结构安全施工以及运营的安全提供可靠信息,对确保结构安全具有重要意义.
中文关键词: 结构变形预测  深度学习  LSTM  正交试验
Abstract:With the vigorous development of social economy, the demand for large buildings such as subways, tunnels, and bridges is growing. Through analyzing the structural deformation data, it can judge the future development trend of the structure so that emergency measures can be taken in advance to prevent the occurrence of disasters. Due to the instability and nonlinearity of deformation monitoring data, the prediction of monitoring data has become a problem in structural monitoring researches. Aiming at the problems of structural deformation prediction models, a long and short-term memory network (LSTM) structural deformation prediction model is proposed based on orthogonal parameter optimization. The long-term memory of the time series can be obtained through the LSTM network structure, and the internal time characteristics of the structural deformation data can be fully mined, the parameters of the LSTM model can be optimized through the orthogonal experiment. Finally, the model was verified by measured data. Experimental results show that the predicted value of the model is closed to the actual monitoring value. Compared with the WNN, DBN-SVR, and GRU models, the average RMSE, MAE, and MAPE are reduced by 56.01%, 52.94%, and 52.78%, respectively. The LSTM structural deformation prediction model based on orthogonal parameter optimization proposed in this study is an effective structural settlement method, which provides reliable information for the safe construction and operation of the structure, and is of great significance to ensure the safety of the structure.
文章编号:7606     中图分类号:    文献标志码:
基金项目:国家重点研发计划(2018YFC0808706)
引用文本:
甘文娟,陈永红,韩静,王亚飞.基于正交参数优化的LSTM结构变形预测模型.计算机系统应用,2020,29(9):212-218
GAN Wen-Juan,CHEN Yong-Hong,HAN Jing,WANG Ya-Fei.Structure Deformation Prediction Model Based on LSTM and Orthogonal Parameter Optimization.COMPUTER SYSTEMS APPLICATIONS,2020,29(9):212-218

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