Yan Xiang
Papers
1
Total Citations
3
H-Index
1
About
Yan Xiang is a leading researcher in structural health monitoring and underwater infrastructure assessment, with a particular focus on dam safety and defect detection. Their most-cited work, "A three-stage identification and quantification for underwater cracks in dams using hybrid feature learning" (2025), introduces an innovative framework that combines advanced signal processing and machine learning to accurately locate, classify, and measure cracks in submerged concrete structures. This three-stage approach—leveraging hybrid feature learning—addresses a critical gap in non-destructive evaluation, enabling more reliable and automated inspection of hard-to-reach dam surfaces. While still early in its citation trajectory, this paper has already garnered attention for its practical methodology, which integrates acoustic or visual data with deep learning to reduce human error and improve quantification precision. Yan’s contributions are particularly significant for aging dam infrastructure worldwide, where timely and accurate crack detection can prevent catastrophic failures. By bridging computational modeling with real-world engineering challenges, Yan Xiang is helping to advance the field toward smarter, data-driven maintenance strategies for critical water-retaining structures.
Research Focus
Key Achievements
Top Papers
- 1