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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: In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

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

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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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A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering Article

Yong-ping DU, Chang-qing YAO, Shu-hua HUO, Jing-xuan LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5,   Pages 658-666 doi: 10.1631/FITEE.1601732

Abstract: We propose a new item-based restricted Boltzmann machine (RBM) approach for CF and use the deep multilayerRBM network structure, which alleviates the data sparsity problem and has excellent ability to extractThe parameters are learned layer by layer in the deep network.The new feature vector discovered by the multilayer RBM network structure is very effective in predicting

Keywords： Restricted Boltzmann machine     Deep network structure     Collaborative filtering     Recommendation system    

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PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 4,   Pages 418-422 doi: 10.1007/s11465-010-0117-7

Abstract: The PID controller is used to control a nonlinear time-variant membrane structure inflation system.Results show that the neural network PID controller can adapt to the changes in system structure parameters

Keywords： PID     neural network     membrane structure    

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: At present, s mostly use deep s to model some of the auxiliary information, and in the process of modelingHowever, these deep algorithms ignore the combined effects of different categories of data, which canAimed at this problem, in this paper we propose a feedforward deep method, called the deep associationEmpirical evidence shows that deep, joint s can provide better performance.

Keywords： Neural network     Deep learning     Deep association neural network (DAN)     Recommendation    

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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

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   Pages 1213-1232 doi: 10.1007/s11709-022-0880-7

Abstract: confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and DeepNeural Network model (artificial neural network (ANN) with double and triple hidden layers).

Keywords： FRCM     deep neural networks     confinement effect     strength model     confined concrete    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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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 networkBefore 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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A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 6,   Pages 1453-1479 doi: 10.1007/s11709-021-0767-z

Abstract: This paper proposes a new Deep Feed-forward Neural Network (DFNN) approach for damage detection in functionallyA trial-and-error procedure is implemented to determine suitable parameters of the network such as the

Keywords： damage detection     deep feed-forward neural networks     functionally graded carbon nanotube-reinforced composite    

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Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1667-3

Abstract:

● Hybrid deep-learning model is proposed for water quality prediction

Keywords： Water quality prediction     Soft-sensor     Anaerobic process     Tree-structured Parzen Estimator    

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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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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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Application of Titanium Alloy Materials for the Pressure-Resistant Structure of Deep Diving Equipment

Jiang Peng, Wang Qi, Zhang Binbin, Wang Yang

Strategic Study of CAE 2019, Volume 21, Issue 6,   Pages 95-101 doi: 10.15302/J-SSCAE-2019.06.018

Abstract:

The development of titanium alloy deep-diving equipment is first summarized, and the desired propertiesof titanium alloy materials are then analyzed considering the characteristics of the deep-sea serviceTechnical challenges faced by the application of deep-diving equipment are also introduced, includingTo satisfy the urgent demand for titanium alloy equipment in the deep-diving field, we propose to further

Keywords： deep diving equipment     pressure-resistant structure     titanium alloy     key technology    

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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 decisionThe proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more

Keywords： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     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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Title Author Date Type Operation

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Journal Article

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

Journal Article

A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering

Yong-ping DU, Chang-qing YAO, Shu-hua HUO, Jing-xuan LIU

Journal Article

PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

Journal Article

DAN: a deep association neural network approach for personalization recommendation

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

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

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Journal Article

A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced

Journal Article

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Journal Article

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

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

Application of Titanium Alloy Materials for the Pressure-Resistant Structure of Deep Diving Equipment

Jiang Peng, Wang Qi, Zhang Binbin, Wang Yang

Journal Article

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

Journal Article

Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Journal Article