Papers
16
Total Citations
1,571
H-Index
11
About
Mohammad R. Jahanshahi is a pioneering researcher at the intersection of computer vision, deep learning, and structural health monitoring, whose work has fundamentally transformed how engineers assess and maintain civil infrastructure. With a career spanning over fifteen years, he has championed the development of automated, non-contact methods for detecting and quantifying structural defects — replacing subjective, labor-intensive visual inspections with intelligent, data-driven systems. Jahanshahi's early contributions established rigorous frameworks for image-based crack detection and measurement, incorporating 3D scene reconstruction and photogrammetry to achieve unprecedented accuracy in assessing structural damage (garnering over 250 citations each). His landmark 2017 work applying convolutional neural networks to corrosion detection — now cited more than 360 times — helped define the standard for deep learning in infrastructure assessment. More recently, he has extended these methods to post-disaster reconnaissance, enabling autonomous multi-class damage classification following earthquakes, and has explored efficient edge-computing solutions through neural network pruning for real-world deployment. His broader vision, encompassing swarm robotics and smart city applications, reflects a sustained commitment to making structural monitoring scalable, objective, and proactive. Jahanshahi's cumulative body of work, with well over 1,500 citations, represents an indispensable foundation for the next generation of resilient, intelligently monitored infrastructure.
Research Focus
Key Achievements
Top Papers
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- 9Reconfigurable swarm robots for structural health monitoring: a brief review35 citations · 2017
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