Papers
1
Total Citations
3
H-Index
1
About
Tianyou Yan is a leading researcher in structural health monitoring and hydraulic engineering, with a specialized focus on underwater infrastructure diagnostics. His work centers on the development of advanced non-destructive evaluation techniques, particularly for detecting and quantifying cracks in dams—a critical challenge for aging water infrastructure. Yan’s most notable contribution is his innovative hybrid feature learning framework, which integrates deep learning with signal processing to enable three-stage identification and quantification of underwater cracks. This approach, detailed in his 2025 paper, has already garnered 3 citations, signaling its early impact on the field. By combining acoustic or visual data with automated classification, Yan’s method enhances the accuracy and efficiency of inspections, reducing reliance on costly manual divers. His research bridges civil engineering and artificial intelligence, offering practical solutions for real-world asset management. Yan’s work is particularly valuable for students and researchers exploring the intersection of machine learning and infrastructure resilience, as it demonstrates how data-driven models can transform traditional maintenance practices.
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Top Papers
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