Tarutal Ghosh Mondal
Purdue University West Lafayette, Missouri University of Science and Technology
Papers
7
Total Citations
218
H-Index
5
About
Tarutal Ghosh Mondal is a leading researcher at the intersection of structural health monitoring, computer vision, and autonomous robotics. His primary contributions lie in developing deep learning frameworks for post-disaster reconnaissance and condition assessment of civil infrastructure. Mondal's most influential work, a 2020 paper on multi-class damage detection for autonomous post-earthquake reconnaissance, has garnered 146 citations, establishing a foundation for rapid, vision-based structural evaluation. He has pioneered the use of unmanned aerial vehicles and swarm robots for spatiotemporal damage assessment, notably introducing methods to reconstruct damage chronology from archival inspection images. His research extends to addressing domain shift challenges in deep learning for bridge element segmentation and exploring multimodal fusion of color and depth features for enhanced damage segmentation. With a trajectory spanning from foundational reviews on reconfigurable swarm robots to cutting-edge spatiotemporal fusion networks, Mondal's work is instrumental in advancing smart city infrastructure resilience. His contributions are widely cited for enabling faster, safer, and more automated structural inspections, directly impacting disaster response and long-term infrastructure maintenance.
Research Focus
Key Achievements
Top Papers
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- 2Reconfigurable swarm robots for structural health monitoring: a brief review35 citations · 2017
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