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Multiscale computation on feedforward neural network and recurrent neural network
Bin LI, Xiaoying ZHUANG
Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6, Pages 1285-1298 doi: 10.1007/s11709-020-0691-7
Keywords: multiscale method constitutive model feedforward neural network recurrent neural network
Frontiers in Energy doi: 10.1007/s11708-023-0891-7
Keywords: machine learning lithium-ion battery state of health neural network artificial intelligence
A constrained neural network model for soil liquefaction assessment with global applicability
Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG
Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5, Pages 1066-1082 doi: 10.1007/s11709-020-0651-2
Keywords: soil liquefaction assessment case history dataset constrained neural network model existing knowledge
Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO
Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10, Pages 1213-1232 doi: 10.1007/s11709-022-0880-7
Keywords: FRCM deep neural networks confinement effect strength model confined concrete
Tanvi SINGH, Mahesh PAL, V. K. ARORA
Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 3, Pages 674-685 doi: 10.1007/s11709-018-0505-3
Keywords: batter piles oblique load test neural network M5 model tree random forest regression ANOVA
Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0736-9
Keywords: fault recognition fault localization multi-sensor relations network analysis graph neural network
Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 3, Pages 378-395 doi: 10.1007/s11709-022-0899-9
Keywords: optimization surrogate models artificial neural network SAP2000 genetic algorithm
An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete
Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG
Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6, Pages 1299-1315 doi: 10.1007/s11709-020-0712-6
Keywords: artificial neural network hybrid fiber reinforced concrete tensile behavior sensitivity analysis stress-strain
Frontiers in Energy doi: 10.1007/s11708-023-0906-4
Keywords: lithium-ion batteries RUL prediction double exponential model neural network Gaussian process regression
A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3
● A novel deep learning framework for short-term water demand forecasting.
Keywords: Short-term water demand forecasting Long-short term memory neural network Convolutional Neural Network
Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7
Keywords: deep reinforcement learning hyper parameter optimization convolutional neural network fault diagnosis
Novel interpretable mechanism of neural networks based on network decoupling method
Frontiers of Engineering Management 2021, Volume 8, Issue 4, Pages 572-581 doi: 10.1007/s42524-021-0169-x
Keywords: neural networks interpretability dynamical behavior network decouple
Deep convolutional neural network for multi-level non-invasive tunnel lining assessment
Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2, Pages 214-223 doi: 10.1007/s11709-021-0800-2
Keywords: concrete structure GPR damage classification convolutional neural network transfer learning
Yang Maosheng,Chen Yueliang,Yu Dazhao
Strategic Study of CAE 2008, Volume 10, Issue 5, Pages 46-50
A prediction model for residual strength of stiffened panels with multiplesite damage based on artificial neural network (ANN) is developed, and the results obtained from thetrained BP model are compared to the analytical and experimental data available in the literature.The results obtained indicate that the neural network model predictions are in the best agreement withexperimental data than any other methods, and the modified linkup models predict better than the linkup model
Keywords: neural network multiple site damage stiffened panel residual strength
Frontiers of Medicine 2022, Volume 16, Issue 3, Pages 496-506 doi: 10.1007/s11684-021-0828-7
Keywords: XGBoost deep neural network healthcare risk prediction
Title Author Date Type Operation
Multiscale computation on feedforward neural network and recurrent neural network
Bin LI, Xiaoying ZHUANG
Journal Article
Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion
Journal Article
A constrained neural network model for soil liquefaction assessment with global applicability
Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG
Journal Article
Development of deep neural network model to predict the compressive strength of FRCM confined columns
Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO
Journal Article
Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree
Tanvi SINGH, Mahesh PAL, V. K. ARORA
Journal Article
A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis
Journal Article
Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neuralnetwork as surrogate model
Journal Article
An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete
Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG
Journal Article
Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neuralnetwork and Gaussian process regression
Journal Article
A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting
Journal Article
A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis
Journal Article
Deep convolutional neural network for multi-level non-invasive tunnel lining assessment
Journal Article
Prediction model for residual strength of stiffened panels with multiple site damage based on artificialneural network
Yang Maosheng,Chen Yueliang,Yu Dazhao
Journal Article