Hyunjun Kim 
- : Development of large-scale synthetic 3D point cloud datasets for vision-based bridge structural condition assessment. In: Advances in Structural Engineering.
- (2023): Framework for rapid characterization of fresh properties of cementitious materials using point cloud and machine learning. In: Construction and Building Materials, v. 400 (Oktober 2023).
- (2024): Evaluation of aggregate segregation in self-consolidating concrete using 3D point cloud analysis. In: Journal of Building Engineering, v. 82 (April 2024).
- (2017): Comparative analysis of image binarization methods for crack identification in concrete structures. In: Cement and Concrete Research, v. 99 (September 2017).
- (2021): Framework for characterizing the time-dependent volumetric properties of aerated cementitious material. In: Construction and Building Materials, v. 284 (Mai 2021).
- (2023): Automated bridge component recognition using close-range images from unmanned aerial vehicles. In: Engineering Structures, v. 274 (Januar 2023).
- (2022): Nontarget-based displacement measurement using LiDAR and camera. In: Automation in Construction, v. 142 (Oktober 2022).
- (2016): A new methodology development for flood fragility curve derivation considering structural deterioration for bridges. In: Smart Structures and Systems, v. 17, n. 1 (Januar 2016).
- (2015): Experimental validation of Kalman filter-based strain estimation in structures subjected to non-zero mean input. In: Smart Structures and Systems, v. 15, n. 2 (Februar 2015).
- (2022): Automated concrete crack evaluation using stereo vision with two different focal lengths. In: Automation in Construction, v. 135 (März 2022).
- (2021): Long-term autogenous healing and re-healing performance in concrete: Evaluation using air-coupled surface-wave method. In: Construction and Building Materials, v. 307 (November 2021).
- (2021): Automated Damage Localization and Quantification in Concrete Bridges Using Point Cloud-Based Surface-Fitting Strategy. In: Journal of Computing in Civil Engineering, v. 35, n. 6 (November 2021).
- (2022): Monitoring of self-healing in concrete with micro-capsules using a combination of air-coupled surface wave and computer-vision techniques. In: Structural Health Monitoring, v. 21, n. 4 (Januar 2022).
- (2020): Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion. In: Structural Health Monitoring, v. 20, n. 2 (Oktober 2020).
- (2021): Automated wireless monitoring system for cable tension forces using deep learning. In: Structural Health Monitoring, v. 20, n. 4 (April 2021).
- (2018): Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning. In: Structural Health Monitoring, v. 18, n. 3 (März 2018).
- (2020): Rheology-based determination of injectable grout fluidity for preplaced aggregate concrete using ultrasonic tomography. In: Construction and Building Materials, v. 260 (November 2020).
- (2020): Automated bridge component recognition from point clouds using deep learning. In: Structural Control and Health Monitoring, v. 27, n. 9 (Juni 2020).
- (2020): Microstructural characteristics of sound absorbable porous cement-based materials by incorporating natural fibers and aluminum powder. In: Construction and Building Materials, v. 243 (Mai 2020).
- (2019): Automated peak picking using region‐based convolutional neural network for operational modal analysis. In: Structural Control and Health Monitoring, v. 26, n. 11 (September 2019).
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