Hang Guan
Papers
1
Total Citations
50
H-Index
1
About
Hang Guan is a researcher specializing in thermal diagnostics and infrastructure monitoring, with a particular focus on non-destructive evaluation techniques. Their most-cited work, "Automatic fault diagnosis algorithm for hot water pipes based on infrared thermal images" (2022), has garnered 50 citations, reflecting its practical significance in urban utility management. Guan’s research integrates infrared thermography with machine learning to detect and classify pipe defects—such as leaks, blockages, or insulation failures—automatically, reducing reliance on manual inspection. This contribution addresses critical challenges in aging water infrastructure, offering cost-effective, real-time monitoring solutions. By developing algorithms that analyze thermal patterns, Guan has advanced the field of predictive maintenance, enabling early fault detection to prevent costly failures and service disruptions. Their work is notable for bridging computer vision and civil engineering, providing a scalable approach for smart city applications. Guan’s impact lies in translating thermal imaging data into actionable diagnostics, with potential extensions to other pipeline systems and industrial settings. This research underscores their role in enhancing the resilience and efficiency of critical infrastructure through innovative, data-driven methodologies.
Research Focus
Key Achievements
Top Papers
- 1