Young-Jin Cha 
- (2024): Deep learning-based structural health monitoring. Dans: Automation in Construction, v. 161 (mai 2024).
- (2023): Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring. Dans: Structural Health Monitoring, v. 23, n. 2 (juillet 2023).
- (2023): Deep learning-based active noise control on construction sites. Dans: Automation in Construction, v. 151 (juillet 2023).
- (2022): Attention-based generative adversarial network with internal damage segmentation using thermography. Dans: Automation in Construction, v. 141 (septembre 2022).
- (2022): Efficient attention-based deep encoder and decoder for automatic crack segmentation. Dans: Structural Health Monitoring, v. 21, n. 5 (mars 2022).
- (2022): Bridge pier scour level quantification based on output-only Kalman filtering. Dans: Structural Health Monitoring, v. 21, n. 5 (mars 2022).
- (2021): Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures. Dans: Automation in Construction, v. 130 (octobre 2021).
- (2015): Modal identification of simple structures with high-speed video using motion magnification. Dans: Journal of Sound and Vibration, v. 345 (juin 2015).
- (2017): Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm. Dans: Structural Health Monitoring, v. 17, n. 2 (janvier 2017).
- (2018): Fully automated vision-based loosened bolt detection using the Viola–Jones algorithm. Dans: Structural Health Monitoring, v. 18, n. 2 (janvier 2018).
- (2020): Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage. Dans: Structural Health Monitoring, v. 20, n. 1 (mars 2020).
- (2020): Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Dans: Automation in Construction, v. 118 (octobre 2020).
- (2018): Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 33, n. 9 (août 2018).
- (2018): Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo-Tagging. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 33, n. 10 (septembre 2018).
- (2017): Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 32, n. 5 (avril 2017).
- (2015): Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 30, n. 5 (avril 2015).
- (2016): Time-Delay Effects on Controlled Seismically Excited Linear and Nonlinear Structures. Dans: International Journal of Structural Stability and Dynamics, v. 16, n. 7 (août 2016).
- (2019): Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer. Dans: Construction and Building Materials, v. 226 (novembre 2019).
- (2015): Active control of highway bridges subject to a variety of earthquake loads. Dans: Earthquake Engineering and Engineering Vibration, v. 14, n. 2 (juin 2015).
- (2019): Deep learning-based automatic volumetric damage quantification using depth camera. Dans: Automation in Construction, v. 99 (mars 2019).
- (2013): Optimal placement of active control devices and sensors in frame structures using multi-objective genetic algorithms. Dans: Structural Control and Health Monitoring, v. 20, n. 1 (janvier 2013).
- (2013): Decentralized output feedback polynomial control of seismically excited structures using genetic algorithm. Dans: Structural Control and Health Monitoring, v. 20, n. 3 (mars 2013).
- (2013): Velocity based semi-active turbo-Lyapunov control algorithms for seismically excited nonlinear smart structures. Dans: Structural Control and Health Monitoring, v. 20, n. 6 (juin 2013).
- (2017): Seismic retrofit of MRF buildings using decentralized semi-active control for multi-target performances. Dans: Earthquake Engineering and Structural Dynamics, v. 46, n. 3 (mars 2017).
- (2016): Vision-based detection of loosened bolts using the Hough transform and support vector machines. Dans: Automation in Construction, v. 71 (novembre 2016).
- (2016): Seismic fragility estimates of a moment-resisting frame building controlled by MR dampers using performance-based design. Dans: Engineering Structures, v. 116 (juin 2016).
- (2014): Direct performance-based design with 200kN MR dampers using multi-objective cost effective optimization for steel MRFs. Dans: Engineering Structures, v. 71 (juillet 2014).
- (2014): Performance Validations of Semiactive Controllers on Large-Scale Moment-Resisting Frame Equipped with 200-kN MR Damper Using Real-Time Hybrid Simulations. Dans: Journal of Structural Engineering (ASCE), v. 140, n. 10 (octobre 2014).
- (2017): Air-coupled impact-echo damage detection in reinforced concrete using wavelet transforms. Dans: Smart Materials and Structures, v. 26, n. 2 (février 2017).
- (2013): Comparative Studies of Semiactive Control Strategies for MR Dampers: Pure Simulation and Real-Time Hybrid Tests. Dans: Journal of Structural Engineering (ASCE), v. 139, n. 7 (juillet 2013).
Structurae coopère avec
