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Journal of Rock Mechanics and Geotechnical Engineering 2023年第4期15卷 886-895页

作者： Xuyan Tan Weizhong Chen Tao Zou Jianping Yang Bowen Du State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan430071China University of Chinese Academy of Sciences Beijing100049China State Key Laboratory of Software Development Environment Beihang UniversityBeijing100191China

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long *** addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is ***,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)*** autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time ***,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 *** a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield *** robustness study is carried out to verify the reliability and the prediction capability of the proposed ***,the ATENet model is compared with some typical models,and the results indicate that it has the best *** model is of great value to predict the realtime evolution trend of tunnel structure.

关键词： Shied tunnel Machine learning Monitoring real-time prediction Data analysis

An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

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Petroleum Research 2023年第3期8卷 370-385页

作者： Chibuzo Cosmas Nwanwe Ugochukwu Ilozurike Duru Charley Anyadiegwu Azunna I.B.Ekejuba Department of Minerals and Petroleum Resources Engineering Technology Federal Polytechnic NekedeOwerriP.M.B.1036OwerriImo StateNigeria Department of Petroleum Engineering Federal University of TechnologyOwerriP.M.B.1526OwerriImo StateNigeria

Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production *** empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent *** machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box *** addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open *** make using the ML models on new datasets *** study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box *** data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for *** analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best *** trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and *** analysis shows that the new model produced the expected effects of physical attributes on ***,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box *** of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

关键词： Flowing bottom-hole pressure real-time prediction Artificial neural network Visible mathematical model Levenberg-marquardt optimization algorithm Hyperbolic tangent activation function Empirical correlations Mechanistic models

ELM-based real-time prediction for Hydrogenolysis Degree of Hydrofining Reaction in Hydrocracking Process 36

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ELM-based Real-time Prediction for Hydrogenolysis Degree of ...

A Framework of LSTM Neural Network Model in Multi-time Scale real-time prediction of Ship Motions in Head Waves

第36届中国控制会议

作者： Guanyu Chen Yalin Wang Yongfei Xue Xiaofeng Yuan Shengwu Zou School of Information Science and Engineering Central South University SINOPEC Jiujiang Company

ISBN: (纸本)9781538629185

Hydrogenolysis degree of hydrofinmg reaction in hydrocracking process is an important index which can reflect the running status of hydrocracking,but it’s difficult to be detected *** this paper,hydrogenolysis degree is redefined with some relevant information which is easier to be detected online,such as hydrogen sulfide and ammonia,and then an ELM-based online prediction method is proposed to forecast the *** that multiple working conditions exist in the long hydrocracking process,steady-state data is selected by double variances method,and a number of data sets are *** data sets include the information of catalyst activity,feed properties,operating parameters and the hydrogenolysis degree calculated using hydrogen sulfide and ammonia content by a given *** specific catalyst activity and feed properties,the relationship between operating parameters and hydrogenolysis degree is established by ELM algorithm,so as to predict the hydrogenolysis degree of hydrofinmg process in real *** simulation results demonstrate that the ELM-based real-time prediction method can effectively predict the hydrogenolysis degree of compounds containing S/N in hydrofinmg process,which is benefit to protect the activity of catalyst and ensure the quality of final products.

关键词： real-time prediction hydrofining hydrogenolysis degree ELM

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船舶力学 2024年第12期28卷 1803-1819页

作者： CHEN Zhan-yang ZHAN Zheng-yong CHANG Shao-ping XU Shao-feng LIU Xing-yun Department of Ocean Engineering Harbin Institute of Technology at WeihaiWeihai 264209China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China Flight Control System Department AVIC Xi'an Flight Automatic Control Research InstituteXi'an 710065China

Ship motions induced by waves have a significant impact on the efficiency and safety of offshore ***-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive ***,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate ***,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed *** numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship *** related experimental data were employed to verify the numerical simulation *** results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.

关键词： deep learning LSTM ship motion real-time prediction irregular waves

real-time predictions of the 2023–2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model

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Science China Earth Sciences 2024年第12期67卷 3709-3726页

作者： Rong-Hua ZHANG Lu ZHOU Chuan GAO Lingjiang TAO School of Marine Sciences Nanjing University of Information Science and TechnologyNanjing 210044China Key Laboratory of Ocean Observation and Forecasting Key Laboratory of Ocean Circulation and WavesInstitute of OceanologyChinese Academy of SciencesQingdao 266071China Laoshan Laboratory Qingdao 266237China University of Chinese Academy of Sciences Beijing 101408China

Following triple La Nina events during 2020–2022,the future evolution of climate conditions over the tropical Pacific has been a focused interest in ENSO-related *** and modeling studies indicate that an El Nino event is occurring in 2023;however,large uncertainties remain in terms of its detailed evolution,and the factors affecting its resultant amplitude remain to be ***,a novel deep learning-based Transformer model is adopted to make real-time predictions for the 2023–2024 climate conditions in the tropical *** key fields vital to the El Nino and Southern Oscillation(ENSO)in the tropical Pacific are collectively and simultaneously utilized in model training and in making predictions;therefore,this purely data-driven model is configured in both training and predicting procedures such that the coupled ocean-atmosphere interactions are adequately *** similar to dynamic models,the prediction procedure is executed in a rolling manner to allow ocean-atmosphere anomaly exchanges month by month;the related key fields during multi-month time intervals(TIs)prior to prediction target months are taken as input predictors,serving as initial conditions to precondition the future evolution more ***-time predictions indicate that the climate conditions in the tropical Pacific are surely to develop into an El Nino state in late ***,sensitivity experiments are conducted to examine how prediction skills are affected by the input predictor specifications,including TIs during which information on initial conditions is retained for making predictions.A comparison with other dynamic coupled models is also made to demonstrate the prediction performance for the 2023–2024 El Nino event.

关键词： Transformer model 3D-Geoformer Coupling representation The 2023–2024 El Nino real-time prediction Performance and evaluation

real-time estimation of the structural utilization level of segmental tunnel lining

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Underground Space 2024年第4期17卷 132-145页

作者： Nicola Gottardi Steffen Freitag Gunther Meschke Institute for Structural Mechanics Ruhr University BochumBochum 44801Germany Institute for Structural Analysis Karlsruhe Institute of TechnologyKarlsruhe 76131Germany

Over the last decades,an expansion of the underground network has been taking place to cope with the increasing amount of moving people and *** a consequence,it is of vital importance to guarantee the full functionality of the tunnel network by means of preventive maintenance and the monitoring of the tunnel lining state over time.A new method has been developed for the real-time prediction of the utilization level in tunnel segmental linings based on input monitoring *** new concept is founded on a framework,which encompasses an offline and an online *** the former,the generation of feedforward neural networks is accomplished by employing synthetically produced *** element simulations of the lining structure are conducted to analyze the structural response under multiple loading *** scenarios are generated by assuming ranges of variation of the model input parameters to account for the uncertainty due to the not fully determined in situ *** and target quantities are identified to better assess the structural utilization of the *** latter phase consists in the application of the methodological framework on input monitored data,which allows for a real-time prediction of the physical quantities deployed for the estimation of the lining *** approach is validated on a full-scale test of segmental lining,where the predicted quantities are compared with the actual ***,it is investigated the influence of artificial noise added to the training data on the overall prediction performances and the benefits along with the limits of the concept are set out.

关键词： Segmental lining Artificial neural networks Structural utilization level real-time prediction Structural health monitoring Monitoring data

A simulation-based software to support the real-time operational parameters selection of tunnel boring machines

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Underground Space 2024年第1期14卷 176-196页

作者： Yaman Zendaki Ba Trung Cao Abdullah Alsahly Steffen Freitag Gunther Meschke Institute for Structural Mechanics Ruhr University BochumBochum 44801Germany Institute for Structural Analysis Karlsruhe Institute of TechnologyKarlsruhe 76131Germany

With the fact that the main operational parameters of the construction process in mechanized tunneling are currently selected based on monitoring data and engineering experience without exploiting the advantages of computer methods,the focus of this work is to develop a simulation-based real-time assistant system to support the selection of operational *** choice of an appropriate set of these parameters(i.e.,the face support pressure,the grouting pressure,and the advance speed)during the operation of tunnel boring machines(TBM)is determined by evaluating different tunneling-induced soil-structure interactions such as the surface settlement,the associated risks on existing structures and the tunnel lining *** evaluate soil-structure behavior,an advanced process-oriented numerical simulation model based on the finite cell method is *** enable the real-time prediction capability of the simulation model for a practical application during the advancement of TBMs,surrogate models based on the Proper Orthogonal Decomposition and Radial Basis Functions(POD-RBF)are *** proposed approach is demonstrated through several synthetic numerical examples inspired by the data of real tunnel *** developed methods are integrated into a user-friendly application called SMART to serve as a support platform for tunnel engineers at construction *** to each user adjustment of the input parameters,i.e.,each TBM driving scenario,approximately two million outputs of soil-structure interactions are quickly predicted and visualized in seconds,which can provide the site engineers with a rough estimation of the impacts of the chosen scenario on structural responses of the tunnel and above ground structures.

关键词： Numerical simulation Surrogate model real-time prediction Proper orthogonal decomposition Radial basis functions TBM operation Smart construction

Online learning method for predicting air environmental information used in agricultural robots

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International Journal of Agricultural and Biological Engineering 2024年第5期17卷 206-212页

作者： Yueting Wang Minzan Li Ronghua Ji Minjuan Wang Yao Zhang Lihua Zheng National Research Center of Intelligent Equipment for Agriculture Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Key Laboratory of Smart Agriculture Systems Ministry of EducationChina Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Sensors Ministry of Agriculture and Rural AffairsBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China

Air environmental information plays an important role during plant growth and reproduction, prompt and accurate prediction of atmospheric environmental data is helpful for agricultural robots to make a timely decision. For efficiency, an online learning method for predicting air environmental information was presented in this work. This method combines the advantages of convolutional neural network (CNN) and experience replay technique: CNN is used to extract features from raw data and predict atmospheric environmental information, experience replay technique can store environmental data over some time and update the hyperparameters of CNN. To validate the effects of this method, this online method was compared with three different predictive methods (including random forest, multi-layer perceptron, and support vector regression) using a public dataset (Jena). According to results, a suitable sample sequence size (e.g., 16) has a smaller number of training sessions and stable results, a larger replay memory size (e.g., 200) can provide enough samples to capture useful features, and 6 d of historical information is the best setting for training predictor. Compared with traditional methods, the method proposed in this study is the only method applied for various conditions.

关键词： online learning method conventional neural network real-time prediction air environmental information

基于物理信息引导深度学习的建筑响应实时预测方法

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Engineering 2024年第4期35卷 140-157页

作者： Ying Zhou Shiqiao Meng Yujie Lou Qingzhao Kong State Key Laboratory of Disaster Reduction in Civil Engineering Tongji UniversityShanghai 200092China

High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitoring,and seismic resilience assessment of *** improve the accuracy and efficiency of structural response prediction,this study proposes a novel physics-informed deep-learning-based realtime structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training *** proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model,thereby enabling higher-precision *** were conducted on a four-story masonry structure,an eleven-story reinforced concrete irregular structure,and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed *** addition,the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation ***,by conducting a comparative experiment,the impact of the range of seismic wave amplitudes on the prediction accuracy was *** experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.

关键词： Structural seismic response prediction Physics information informed real-time prediction Earthquake engineering Data-driven machine learning