Gangbing Song
University of Houston, Naval Postgraduate School, University of Akron
Papers
14
Total Citations
478
H-Index
8
About
Gangbing Song is a prominent researcher whose work spans structural health monitoring, smart sensing technologies, robotics, and control systems. He is perhaps best known for pioneering innovative approaches to bolt looseness detection—a critical challenge in structural integrity—most notably his landmark 2018 paper "Tapping and listening," which has garnered over 160 citations and introduced a novel percussion-based monitoring paradigm. Building on this foundation, Song has led the development of increasingly sophisticated diagnostic systems, including machine learning-enhanced methods such as entropy-driven active sensing with ensemble learning and CBAM-enhanced lightweight ResNet architectures, reflecting his commitment to bridging signal processing with artificial intelligence. His contributions extend beyond bolted structures: Song has investigated sand deposition detection in pipelines using voice recognition and support vector machines, and explored subsea inspection using piezoceramic transducers, demonstrating a consistent focus on challenging, real-world engineering environments. His early career contributions in nonholonomic mobile robot control further reveal the breadth of his technical expertise. With a body of work accumulating hundreds of citations and spanning two decades, Song's research has meaningfully advanced the fields of structural monitoring and intelligent sensing, offering practical solutions that improve safety across civil, mechanical, and subsea infrastructure.
Research Focus
Key Achievements
Top Papers
- 1Tapping and listening: a new approach to bolt looseness monitoring160 citations · 2018
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- 7Grasping Force Control of Robotic Gripper With High Stiffness19 citations · 2021
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