Yangtao Li

Hohai University, Nanjing Forestry University

Papers

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About

Yangtao Li is a leading researcher in the intersection of civil infrastructure safety and artificial intelligence, with a primary focus on automated defect detection in hydraulic tunnels and dams. His most impactful work, "A robust real‐time method for identifying hydraulic tunnel structural defects using deep learning and computer vision" (2022), has garnered 77 citations, establishing a foundational framework for combining convolutional neural networks with real-time image analysis to detect cracks, leaks, and other structural anomalies in challenging underwater environments. Li further advanced this field with his three-stage hybrid feature learning approach (2025), which enables both identification and quantification of underwater cracks in dams—a critical step toward predictive maintenance. His contributions are notable for bridging the gap between computer vision algorithms and practical geotechnical engineering, offering scalable solutions that reduce reliance on manual inspections. By integrating deep learning with domain-specific structural mechanics, Li’s work has significant implications for extending the lifespan of aging hydraulic infrastructure and preventing catastrophic failures. His research continues to influence both academic studies and industry applications in smart infrastructure monitoring.