Dr. Salar Kamari
Assistant Professor
Bio
Dr. Kamari is an Assistant Professor in the School of Construction and Design at USM. He received his M.Sc. from Istanbul Technical University in Civil Structural Engineering and obtained his Ph.D. in Construction Science from Texas A&M University. Dr. Kamari performs cutting-edge multidisciplinary research on rapid and automated perception techniques, including digital twinning and point cloud segmentation using large-scale visual data.
His areas of focus include:
Resilient infrastructure system analysis through big visual data
Reality capturing and point cloud processing
Scanning-to-BIM driven rapid semantic scene understanding
Robust imaging-to-simulation driven risk assessment models
Dr. Kamari has published numerous articles in top-ranked journals and has presented his research at prestigious conferences in the area of Construction Engineering and Management.
- PHD - Texas A & M University-College Station (2022)
AEC 450 Building Information Modeling
AEC 254 Estimating 1
COSC 461, Building Information Modeling Systems (TA at Texas A&M)
- Analyzing Safety Risk Imposed by Jobsite Debris to Nearby Built Environments Using Geometric Digital Twins and Vision-Based Deep Learning, Journal of Computing in Civil Engineering, 2022, https://doi.org/10.1061/(ASCE)CP.1943-5487.0001044
- AI-based Risk Assessment for Construction Site Disaster Preparedness through Deep Learning-based Digital Twinning, Automation in Construction, , https://doi.org/10.1016/j.autcon.2021.104091
- Large Scale Visual Data-Driven Probabilistic Risk Assessment of Utility Poles regarding the Vulnerability of Power Distribution Infrastructure System, Journal of Construction Engineering and Management, 2021, https://doi.org/10.1061/(ASCE)CO.1943-7862.0002153
- Vision-based Volumetric Measurements via Deep Learning-based Point Cloud Segmentation for Material Management in Jobsites, Automation in Construction, 2020, https://doi.org/10.1016/j.autcon.2020.103430
- Automated Content-Based Filtering for Enhanced Vision-based Documentation in Construction toward Exploiting Big Visual Data from Drones, Automation in Construction, 2019, https://doi.org/10.1016/j.autcon.2019.102831