Dezhi Li
Papers
1
Total Citations
7
H-Index
1
About
Dezhi Li is a leading researcher in structural health monitoring, with a particular focus on underwater infrastructure safety. His work bridges advanced deep learning and signal processing techniques to solve critical challenges in civil engineering. Li’s most notable contribution is the development of the Crackwave R-convolutional neural network, a pioneering fusion model that integrates discrete wavelet transform with deep learning for underwater dam crack detection. This innovation, published in 2025 and already garnering 7 citations, addresses the urgent need for reliable, automated inspection methods to prevent catastrophic structural failures. By enabling precise identification of cracks in challenging underwater environments, Li’s research directly enhances the long-term stability and safety of dams and other hydraulic structures. His work represents a significant leap forward in non-destructive evaluation, combining theoretical rigor with practical application. Li’s contributions are particularly impactful for researchers and engineers working at the intersection of artificial intelligence and civil infrastructure, offering a scalable, data-driven approach to maintenance and risk assessment. His ongoing efforts continue to shape the future of smart structural monitoring systems.
Research Focus
Key Achievements
Top Papers
- 1