Young-Jin Cha
Young-Jin Cha
Associate Professor, University of Manitoba
Verified email at - Homepage
Cited by
Cited by
Deep learning-based crack damage detection using convolutional neural networks
YJ Cha, W Choi, O Buyukozturk
Computer-Aided Civil and Infrastructure Engineering 32 (5), 361-378, 2016
Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types
YJ Cha, W Choi, G Suh, S Mahmoudkhani, O Büyüköztürk
Computer‐Aided Civil and Infrastructure Engineering, 2017
Modal identification of simple structures with high-speed video using motion magnification
JG Chen, N Wadhwa, YJ Cha, F Durand, WT Freeman, O Buyukozturk
Journal of Sound and Vibration 345, 58-71, 2015
Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo‐tagging
D Kang, YJ Cha
Computer‐Aided Civil and Infrastructure Engineering, 2018
Vision-based detection of loosened bolts using the Hough transform and support vector machines
YJ Cha, K You, W Choi
Automation in Construction 71, 181-188, 2016
Structural damage detection using modal strain energy and hybrid multiobjective optimization
YJ Cha, O Buyukozturk
Computer‐Aided Civil and Infrastructure Engineering 30 (5), 347-358, 2015
Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters
YJ Cha, JG Chen, O Büyüköztürk
Engineering Structures 132, 300-313, 2017
Deep learning-based automatic volumetric damage quantification using depth camera
GH Beckman, D Polyzois, YJ Cha
Automation in Construction 99, 114-124, 2019
SDDNet: Real-time crack segmentation
W Choi, YJ Cha
IEEE Transactions on Industrial Electronics, 2019
Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning
D Kang, SS Benipal, DL Gopal, YJ Cha
Automation in Construction 118, 103291, 2020
Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures
YJ Cha, AK Agrawal, Y Kim, AM Raich
Expert Systems with Applications 39 (9), 7822-7833, 2012
Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage
Z Wang, YJ Cha
Structural Health Monitoring 20 (1), 406-425, 2021
Optimal placement of active control devices and sensors in frame structures using multi‐objective genetic algorithms
YJ Cha, A Raich, L Barroso, A Agrawal
Structural Control and Health Monitoring 20 (1), 16-44, 2013
Comparative studies of semiactive control strategies for MR dampers: pure simulation and real-time hybrid tests
YJ Cha, J Zhang, AK Agrawal, B Dong, A Friedman, SJ Dyke, J Ricles
Journal of Structural Engineering 139 (7), 1237-1248, 2013
Structural modal identification through high speed camera video: Motion magnification
JG Chen, N Wadhwa, YJ Cha, F Durand, WT Freeman, O Buyukozturk
Topics in Modal Analysis I, Volume 7, 191-197, 2014
Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer
R Ali, YJ Cha
Construction and Building Materials 226, 376-387, 2019
Large-scale real-time hybrid simulation for evaluation of advanced damping system performance
A Friedman, SJ Dyke, B Phillips, R Ahn, B Dong, Y Chae, N Castaneda, ...
Journal of Structural Engineering 141 (6), 04014150, 2015
Fully automated vision-based loosened bolt detection using the Viola–Jones algorithm
L Ramana, W Choi, YJ Cha
Structural Health Monitoring 18 (2), 422-434, 2019
Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm
YJ Cha, Z Wang
Structural Health Monitoring 17 (2), 313-324, 2018
Time delay effects on large-scale MR damper based semi-active control strategies
YJ Cha, AK Agrawal, SJ Dyke
Smart Materials and Structures 22 (1), 015011, 2012
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