Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures

[1]  Yi-Zhou Lin,et al.  Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning , 2017, Comput. Aided Civ. Infrastructure Eng..

[2]  Hojjat Adeli,et al.  SPARSE MATRIX ALGORITHM FOR MINIMUM WEIGHT DESIGN OF LARGE STRUCTURES , 1996 .

[3]  Jian Zhang,et al.  Convolutional Sparse Autoencoders for Image Classification , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Oral Büyüköztürk,et al.  Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types , 2018, Comput. Aided Civ. Infrastructure Eng..

[5]  Miki Haseyama,et al.  Distress classification of class-imbalanced inspection data via correlation-maximizing weighted extreme learning machine , 2018, Adv. Eng. Informatics.

[6]  Wei Zhang,et al.  Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization , 2018, Comput. Aided Civ. Infrastructure Eng..

[7]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Miki Haseyama,et al.  Distress Classification of Road Structures via Adaptive Bayesian Network Model Selection , 2017 .

[9]  Yang Liu,et al.  Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network , 2017, Comput. Aided Civ. Infrastructure Eng..

[10]  Rama Chellappa,et al.  Deep Multitask Learning for Railway Track Inspection , 2015, IEEE Transactions on Intelligent Transportation Systems.

[11]  Michael Elad,et al.  Convolutional Neural Networks Analyzed via Convolutional Sparse Coding , 2016, J. Mach. Learn. Res..

[12]  Cong Zhou,et al.  Damage Identification for Hysteretic Structures Using a Mode Decomposition Method , 2018, Comput. Aided Civ. Infrastructure Eng..

[13]  Hojjat Adeli,et al.  Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete , 2017 .

[14]  Hojjat Adeli,et al.  A novel unsupervised deep learning model for global and local health condition assessment of structures , 2018 .

[15]  Nikolaos Doulamis,et al.  Combined Convolutional Neural Networks and Fuzzy Spectral Clustering for Real Time Crack Detection in Tunnels , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[16]  Wei Xia,et al.  An Approach for Extracting Road Pavement Disease from HD Camera Videos by Deep Convolutional Networks , 2018, 2018 International Conference on Audio, Language and Image Processing (ICALIP).

[17]  Jianyu Chen,et al.  Supervised classification of hyperspectral images using local-receptive-fields-based kernel extreme learning machine , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[18]  Hubert Cecotti,et al.  Deep Random Vector Functional Link Network for handwritten character recognition , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[19]  Yihong Gong,et al.  Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Chi-Man Vong,et al.  Local Receptive Fields Based Extreme Learning Machine , 2015, IEEE Computational Intelligence Magazine.

[21]  H. Adeli,et al.  A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures , 2015 .

[22]  Hojjat Adeli,et al.  A novel machine learning‐based algorithm to detect damage in high‐rise building structures , 2017 .

[23]  Nanning Zheng,et al.  Constructing Deep Sparse Coding Network for image classification , 2017, Pattern Recognit..

[24]  Bolei Zhou,et al.  Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.

[25]  Paul W. Fieguth,et al.  A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure , 2015, Adv. Eng. Informatics.

[26]  Eduardo Zalama Casanova,et al.  Road Crack Detection Using Visual Features Extracted by Gabor Filters , 2014, Comput. Aided Civ. Infrastructure Eng..

[27]  Reginald R. Souleyrette,et al.  Optimizing the Alignment of Inspection Data from Track Geometry Cars , 2015, Comput. Aided Civ. Infrastructure Eng..

[28]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Gordon Wetzstein,et al.  Fast and flexible convolutional sparse coding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Kelvin C. P. Wang,et al.  Pavement Crack Width Measurement Based on Laplace's Equation for Continuity and Unambiguity , 2018, Comput. Aided Civ. Infrastructure Eng..

[31]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[32]  Tsai-Yun Liao,et al.  On‐Line Vehicle Routing Problems for Carbon Emissions Reduction , 2017, Comput. Aided Civ. Infrastructure Eng..

[33]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[34]  Victor C. M. Leung,et al.  Extreme Learning Machines [Trends & Controversies] , 2013, IEEE Intelligent Systems.

[35]  Khalid M. Mosalam,et al.  Deep Transfer Learning for Image‐Based Structural Damage Recognition , 2018, Comput. Aided Civ. Infrastructure Eng..

[36]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[37]  Dejan J. Sobajic,et al.  Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.

[38]  Filip Sroubek,et al.  Fast convolutional sparse coding using matrix inversion lemma , 2016, Digit. Signal Process..

[39]  Anders P. Eriksson,et al.  Fast Convolutional Sparse Coding , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[41]  Brendt Wohlberg,et al.  Convolutional sparse representation of color images , 2016, 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI).

[42]  Yimin D. Zhang,et al.  Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[43]  Mohammad R. Jahanshahi,et al.  NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion , 2018, IEEE Transactions on Industrial Electronics.

[44]  Hong Zhang,et al.  Automatic Visual Defect Detection Using Texture Prior and Low-Rank Representation , 2018, IEEE Access.

[45]  B. Mercier,et al.  A dual algorithm for the solution of nonlinear variational problems via finite element approximation , 1976 .

[46]  Hadi Meidani,et al.  Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[47]  Hojjat Adeli,et al.  Effect of general sparse matrix algorithm on optimization of space structures , 1995 .

[48]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[49]  Brendt Wohlberg,et al.  Efficient Algorithms for Convolutional Sparse Representations , 2016, IEEE Transactions on Image Processing.

[50]  Miki Haseyama,et al.  Automatic estimation of deterioration level on transmission towers via deep extreme learning machine based on local receptive field , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[51]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[52]  Sang-Kyun Woo,et al.  Development of the Corrosion Deterioration Inspection Tool for Transmission Tower Members , 2016 .

[53]  David Zhang,et al.  A Survey of Sparse Representation: Algorithms and Applications , 2015, IEEE Access.

[54]  Boguslaw Cyganek,et al.  Image recognition with deep neural networks in presence of noise - Dealing with and taking advantage of distortions , 2017, Integr. Comput. Aided Eng..

[55]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[56]  Yicheng Li,et al.  A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects , 2018, Comput. Aided Civ. Infrastructure Eng..

[57]  Leonardo Franco,et al.  Layer multiplexing FPGA implementation for deep back-propagation learning , 2017, Integr. Comput. Aided Eng..

[58]  Peter Söderholm,et al.  Data Analysis for Condition‐Based Railway Infrastructure Maintenance , 2015, Qual. Reliab. Eng. Int..

[59]  Siddhartha Kumar Khaitan,et al.  Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection , 2017 .

[60]  Vikram Pakrashi,et al.  Texture Analysis Based Damage Detection of Ageing Infrastructural Elements , 2013, Comput. Aided Civ. Infrastructure Eng..

[61]  H. Adeli,et al.  Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures , 2015 .

[62]  Hongming Zhou,et al.  Extreme Learning Machines [Trends & Controversies] , 2013 .

[63]  Xiao Liang,et al.  Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization , 2018, Comput. Aided Civ. Infrastructure Eng..

[64]  Walid Tizani,et al.  Advances and challenges in computing in civil and building engineering , 2011, Adv. Eng. Informatics.