Xueli Ren
Papers
1
Total Citations
41
H-Index
1
About
Xueli Ren is a researcher at the forefront of applying deep learning to structural health monitoring, with a primary focus on automated defect detection in civil infrastructure. Ren’s most influential work centers on the development of advanced computer vision techniques for identifying surface cracks in concrete structures—a critical indicator of durability and service performance in bridges and buildings. In their highly cited 2020 study, Ren introduced an innovative approach using the ResNeXt architecture combined with sophisticated postprocessing to automatically detect and segment cracks from real-world concrete surface images. This work directly addresses the limitations of traditional, labor-intensive artificial visual inspections, offering a more efficient, accurate, and scalable solution for infrastructure assessment. With 41 citations, this paper has become a key reference in the growing field of AI-driven non-destructive evaluation. By bridging the gap between state-of-the-art deep learning models and practical engineering challenges, Ren’s contributions are paving the way for smarter, safer, and more resilient infrastructure management, making their research essential reading for students and professionals in structural engineering and applied AI.
Research Focus
Key Achievements
Top Papers
- 1