Young-Jin Cha

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Oral BuyukozturkProfessor of Civil and Environmental Engineering, Masachusetts Institute of TechnologyVerified email at mit.edu
Anil AgrawalProfessor of Civil Engineering, The City College of New YorkVerified email at ccny.cuny.edu
Justin G. ChenMassachusetts Institute of TechnologyVerified email at mit.edu
Dong Ho KangPh.D.Verified email at myumanitoba.ca
Yeesock KimProfessor of Civil Engineering and Construction Management, California Baptist UniversityVerified email at calbaptist.edu
Shirley J. DykeProfessor of Mechanical Engineering, Professor of Civil Engineering, Purdue UniversityVerified email at purdue.edu
Zilong WangCivil Engineering, University of ManitobaVerified email at myumanitoba.ca
Anne RaichAssociate Professor, Valparaiso UniversityVerified email at valpo.edu
James RiclesBruce G. Johnston Professor, Lehigh UniversityVerified email at lehigh.edu
Fredo DurandProfessor of Computer Science, MITVerified email at mit.edu
William T. FreemanProfessor of Computer Science, MITVerified email at mit.edu
Neal WadhwaGoogle ResearchVerified email at mit.edu
Billie F. Spencer, Jr.Professor of Civil and Environmental Engineering, University of Illinois at Urbana-ChampaignVerified email at illinois.edu
Brian PhillipsAssociate Professor, University of FloridaVerified email at essie.ufl.edu
Esmaeil GhorbaniUniversity of ManitobaVerified email at myumanitoba.ca
Richard ChristensonProfessor, Department of Civil & Environmental Engineering, University of ConnecticutVerified email at engr.uconn.edu
Luciana BarrosoAssociate Professor, Zachry Dept. Civil Engineering, Texas A&M UniversityVerified email at civil.tamu.edu
JONG-WHA BAIProfessor, California Baptist UniversityVerified email at calbaptist.edu
Zhaoshuo Jiang, Ph.D., P.E., LEED APAssociate Professor, San Francisco State UniversityVerified email at sfsu.edu
Richard SauseLehigh UniversityVerified email at lehigh.edu

Young-Jin Cha

Associate Professor, University of Manitoba

Verified email at umanitoba.ca - Homepage

Deep learning Automation Monitoring Dynamics & Control


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	1797	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	728	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	376	2015
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	189	2015
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	184	2016
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	173	2018
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	124	2017
Deep learning-based automatic volumetric damage quantification using depth camera GH Beckman, D Polyzois, YJ Cha Automation in Construction 99, 114-124, 2019	122	2019
SDDNet: Real-time crack segmentation W Choi, YJ Cha IEEE Transactions on Industrial Electronics, 2019	100	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	93	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	90	2012
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	73	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	71	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	66	2014
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	64	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	62	2019
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	60	2021
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	60	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	52	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	51	2012

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