Kang Shua Yeo
Papers
1
Total Citations
14
H-Index
1
About
Kang Shua Yeo is a researcher at the forefront of automated infrastructure inspection, specializing in the fusion of optical and laser-based sensing technologies for defect detection in civil structures. His most cited work, "Automatic optical & laser-based defect detection and classification in brick masonry walls" (2016), introduces a real-time system that integrates data from vision and laser sensors to identify and classify defects in brick masonry. At the heart of this contribution is a novel Support Vector Machine (SVM) algorithm, which Yeo developed into a Defect Finding Classification Model (DFCM) capable of automatically categorizing defect types with high accuracy. This work has garnered 14 citations, reflecting its practical significance for non-destructive testing and structural health monitoring. Yeo’s research addresses a critical need in aging infrastructure management, offering a scalable, automated alternative to manual inspection. His achievements demonstrate a strong commitment to advancing sensor fusion and machine learning applications in civil engineering, making his work a valuable reference for students and researchers exploring intelligent inspection systems.
Research Focus
Key Achievements
Top Papers
- 1