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作 者:Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen
机构地区:[1]State Key Laboratory of Software Development Environment,Beihang University,Beijing,China [2]State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan,China
出 处:《Deep Underground Science and Engineering》2025年第1期72-82,共11页深地科学(英文)
基 金:National Key Research and Development Program of China,Grant/Award Number:2018YFB2101003;National Natural Science Foundation of China,Grant/Award Numbers:51991395,U1806226,51778033,51822802,71901011,U1811463,51991391;Science and Technology Major Project of Beijing,Grant/Award Number:Z191100002519012。
摘 要:It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.
关 键 词:autoencoder deep learning structural health monitoring time-series prediction
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