Papers
1
Total Citations
22
H-Index
1
About
Jinwen Tan is a leading researcher in structural health monitoring and civil infrastructure inspection, with a primary focus on applying machine vision and artificial intelligence to detect and assess defects in concrete structures. His most-cited work, "Automatic crack inspection for concrete bridge bottom surfaces based on machine vision" (2017, 22 citations), addresses a critical challenge in bridge maintenance: the need for periodic, accurate, and non-destructive crack detection. Tan’s major contribution lies in developing automated image-based methods that replace traditional manual inspection, significantly improving both safety and efficiency. By leveraging computer vision algorithms, his research enables the early identification of cracks—the most common and dangerous defect in low-cost, high-plasticity concrete bridges—before they worsen and compromise structural integrity. This work has been widely cited by engineers and researchers seeking practical, scalable solutions for aging infrastructure. Tan’s achievements underscore his role in bridging the gap between advanced computational techniques and real-world civil engineering needs, making him a key figure in the ongoing digital transformation of infrastructure maintenance.
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Top Papers
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