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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

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

real-time prediction of Seepage Field Based on Extended Kalman Filter

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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Real-time Prediction of Seepage Field Based on Extended Kalm...

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

2011 World Congress on Engineering and Technology(CET 2011)

作者： Yuan Wang 1 2 Hua Jin 1 2 Lijie Ouyang 1 2 1.Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering 2.Research Institute of Tunnels and Subways of Hohai Univ.Nanjing China

Selecting accurate hydraulic conductivity is critical to calculating correctly seepage *** of geotechnical hydraulic conductivity with spatial randomness and its errors in measurement and computation,the extended Kalman filter （EKF） is proposed to its back *** conjunction with the finite element method （FEM） of seepage,details of the EKF are introduced in this *** FORTRAN programs,the hydraulic conductivity is revised according to the real-time monitoring data during tunnel excavation,and illustrates the EKF merits of considerable convergence and tolerable accuracy.

关键词： extended Kalman filter finite element method seepage field real-time prediction

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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

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