Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of di...Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of disaster prediction.In contrast to other SHM datasets,the monitoring data specific to tunnel engineering exhibits pronounced spatiotemporal correlations.Nevertheless,most methodologies fail to adequately combine these types of correlations.Hence,the objective of this study is to develop spatiotemporal recurrent neural network(ST-RNN)model,which exploits spatiotemporal information to effectively impute missing data within tunnel monitoring systems.ST-RNN consists of two moduli:a temporal module employing recurrent neural network(RNN)to capture temporal dependencies,and a spatial module employing multilayer perceptron(MLP)to capture spatial correlations.To confirm the efficacy of the model,several commonly utilized methods are chosen as baselines for conducting comparative analyses.Furthermore,parametric validity experiments are conducted to illustrate the efficacy of the parameter selection process.The experimentation is conducted using original raw datasets wherein various degrees of continuous missing data are deliberately introduced.The experimental findings indicate that the ST-RNN model,incorporating both spatiotemporal modules,exhibits superior interpolation performance compared to other baseline methods across varying degrees of missing data.This affirms the reliability of the proposed model.展开更多
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 externa...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.展开更多
Stimulated Raman scattering(SRS)is one of the main instabilities affecting success of fusion ignition.Here,we study the relationship between Raman growth and Landau damping with various distribution functions combinin...Stimulated Raman scattering(SRS)is one of the main instabilities affecting success of fusion ignition.Here,we study the relationship between Raman growth and Landau damping with various distribution functions combining the analytic formulas and Vlasov simulations.The Landau damping obtained by Vlasov-Poisson simulation and Raman growth rate obtained by Vlasov-Maxwell simulation are anti-correlated,which is consistent with our theoretical analysis quantitatively.Maxwellian distribution,flattened distribution,and bi-Maxwellian distribution are studied in detail,which represent three typical stages of SRS.We also demonstrate the effects of plateau width,hot-electron fraction,hot-to-cold electron temperature ratio,and collisional damping on the Landau damping and growth rate.They gives us a deep understanding of SRS and possible ways to mitigate SRS through manipulating distribution functions to a high Landau damping regime.展开更多
Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters,so monitoring is required.Data collected by structural health monitoring(SHM)systems are easily affected by many fa...Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters,so monitoring is required.Data collected by structural health monitoring(SHM)systems are easily affected by many factors,such as temperature,sensor fluctuation,sensor failure,which can introduce a lot of noise,increasing the difficulty of structural anomaly identification.To address this problem,this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder(CIDAE),a denoising autoencoder-based deep learning model for SHM of civil infrastructure.As a case study,the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation.Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted.It is concluded that CIDAE is superior to traditional methods.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.51991395 and 42293355)geological survey project of China Geological Survey:Support for Geo-hazard monitoring,early warning and prevention(Grant No.DD20230085).
文摘Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of disaster prediction.In contrast to other SHM datasets,the monitoring data specific to tunnel engineering exhibits pronounced spatiotemporal correlations.Nevertheless,most methodologies fail to adequately combine these types of correlations.Hence,the objective of this study is to develop spatiotemporal recurrent neural network(ST-RNN)model,which exploits spatiotemporal information to effectively impute missing data within tunnel monitoring systems.ST-RNN consists of two moduli:a temporal module employing recurrent neural network(RNN)to capture temporal dependencies,and a spatial module employing multilayer perceptron(MLP)to capture spatial correlations.To confirm the efficacy of the model,several commonly utilized methods are chosen as baselines for conducting comparative analyses.Furthermore,parametric validity experiments are conducted to illustrate the efficacy of the parameter selection process.The experimentation is conducted using original raw datasets wherein various degrees of continuous missing data are deliberately introduced.The experimental findings indicate that the ST-RNN model,incorporating both spatiotemporal modules,exhibits superior interpolation performance compared to other baseline methods across varying degrees of missing data.This affirms the reliability of the proposed model.
基金National Key Research and Development Program of China,Grant/Award Number:2018YFB2101003National Natural Science Foundation of China,Grant/Award Numbers:51991395,U1806226,51778033,51822802,71901011,U1811463,51991391Science and Technology Major Project of Beijing,Grant/Award Number:Z191100002519012。
文摘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.
基金supported by the National Key Research and Development Program of China(2018YFB2101003)the National Natural Science Foundation of China(51991395,51991391,71901011,and U1811463)。
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA25050700)the National Natural Science Foundation of China(Grant Nos.11805062,11875091 and 11975059)+1 种基金the Science Challenge Project(Grant No.TZ2016005)the Natural Science Foundation of Hunan Province,China(Grant No.2020JJ5029)。
文摘Stimulated Raman scattering(SRS)is one of the main instabilities affecting success of fusion ignition.Here,we study the relationship between Raman growth and Landau damping with various distribution functions combining the analytic formulas and Vlasov simulations.The Landau damping obtained by Vlasov-Poisson simulation and Raman growth rate obtained by Vlasov-Maxwell simulation are anti-correlated,which is consistent with our theoretical analysis quantitatively.Maxwellian distribution,flattened distribution,and bi-Maxwellian distribution are studied in detail,which represent three typical stages of SRS.We also demonstrate the effects of plateau width,hot-electron fraction,hot-to-cold electron temperature ratio,and collisional damping on the Landau damping and growth rate.They gives us a deep understanding of SRS and possible ways to mitigate SRS through manipulating distribution functions to a high Landau damping regime.
基金supported by the National Natural Science Foundation of China(Grant Nos.51991395,51991391,and U1811463)the S&T Program of Hebei,China(No.225A0802D).
文摘Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters,so monitoring is required.Data collected by structural health monitoring(SHM)systems are easily affected by many factors,such as temperature,sensor fluctuation,sensor failure,which can introduce a lot of noise,increasing the difficulty of structural anomaly identification.To address this problem,this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder(CIDAE),a denoising autoencoder-based deep learning model for SHM of civil infrastructure.As a case study,the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation.Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted.It is concluded that CIDAE is superior to traditional methods.
