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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis.First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingSecond, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-termACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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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

Abstract: Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential

Keywords： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 814-828 doi: 10.1007/s11465-021-0650-6

Abstract: Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspectiveTo address this issue, this paper explores a decision-tree-structured neural network, that is, the deepconvolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings.The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision

Keywords： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network    

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Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 11,   Pages 930-939 doi: 10.1631/FITEE.1500125

Abstract: In this paper we propose a novel method to estimate head pose based on a deep convolutional neural networkThen two convolutional neural networks are set up to train the head pose classifier and then comparedBefore training the network, two reasonable strategies including shift and zoom are executed to prepare

Keywords： Head pose estimation     Deep convolutional neural network     Multiclass classification    

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Automated identification of steel weld defects, a convolutional neural network improved machine learning

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 2,   Pages 294-308 doi: 10.1007/s11709-024-1045-7

Abstract: Classic and convolutional neural network-enhanced algorithms were used to classify, the extracted featuresThe convolutional neural network-enhanced support vector machine (SVM) outperformed six other algorithms

Keywords： steel weld     machine learning     convolutional neural network     weld defect detection     classification task    

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Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0692-4

Abstract: pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutionalneural network.

Keywords： axial piston pump     fault diagnosis     convolutional neural network     multi-sensor data fusion    

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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

Abstract:

● 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    

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Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 305-317 doi: 10.1007/s11709-021-0725-9

Abstract: this challenge, this paper presents a method for automating concrete damage classification using a deepconvolutional neural network.The convolutional neural network was designed after an experimental investigation of a wide number ofTo increase the network robustness compared to images in real-world situations, different image configurationsmodel, with the highest validation accuracy of approximately 94%, was selected as the most suitable network

Keywords： concrete structure     infrastructures     visual inspection     convolutional neural network     artificial intelligence    

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Crack identification in concrete, using digital image correlation and neural network

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 4,   Pages 536-550 doi: 10.1007/s11709-024-1013-2

Abstract: Digital image correlation (DIC) technology can provide a large amount of experimental data, and neuralnetwork (NN) can process very rich data.Therefore, NN, including convolutional neural networks (CNN) and back propagation neural networks (BP

Keywords： digital image correlation     convolutional neural network     back propagation neural neural network     damage    

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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neuralnetwork

Wenxuan CAO; Junjie LI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1378-1396 doi: 10.1007/s11709-022-0855-8

Abstract: The graph convolutional neural network (GCN) was used to segment the stitched image.The GCN’s m-IOU is 24.02% higher than Fully convolutional networks (FCN), proving that GCN has

Keywords： underwater cracks     remote operated vehicle     image stitching     image segmentation     graph convolutionalneural network    

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Efficient, high-resolution topology optimization method based on convolutional neural networks

Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 1,   Pages 80-96 doi: 10.1007/s11465-020-0614-2

Abstract: efficient, high-resolution topology optimization method is developed based on the super-resolution convolutionalneural network (SRCNN) technique in the framework of SIMP.

Keywords： topology optimization     convolutional neural network     high resolution     density-based    

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 3,   Pages 393-410 doi: 10.1007/s11709-024-1060-8

Abstract: structural damage in full-scale structures using surrogate models generated from incomplete modal data and deepneural networks (DNNs).

Keywords： vibration-based damage detection     deep neural network     full-scale structures     finite element model updating    

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Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 6, doi: 10.1007/s11783-021-1430-6

Abstract:

• UV-vis absorption analyzer was applied in drainage type online recognition.

Keywords： Drainage online recognition     UV-vis spectra     Derivative spectrum     Convolutional neural network    

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Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 882-895 doi: 10.1007/s11709-022-0859-4

Abstract: In this case, the convolutional neural network (CNN) provides a better alternative.sample database for slope stability analysis reaches more than 99%, and the comparisons with the BP neuralnetwork show that the CNN has significant superiority in slope stability evaluation.

Keywords： slope stability     limit equilibrium method     convolutional neural network     database for slopes     big data    

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: These include support-vector machine model and various deep convolutional neural network models, namely

Keywords： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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Title Author Date Type Operation

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

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Journal Article

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Journal Article

Automated identification of steel weld defects, a convolutional neural network improved machine learning

Journal Article

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Journal Article

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Journal Article

Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Journal Article

Crack identification in concrete, using digital image correlation and neural network

Journal Article

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neuralnetwork

Wenxuan CAO; Junjie LI

Journal Article

Efficient, high-resolution topology optimization method based on convolutional neural networks

Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG

Journal Article

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

Journal Article

Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Journal Article

Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

Journal Article

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Journal Article