Papers
2
Total Citations
14
H-Index
2
About
Ronghua Zhu is a researcher at the forefront of intelligent systems and optimization algorithms, with key contributions in underwater structural inspection and metaheuristic computing. Their work on the YOLOX-DG robotic detection system addresses a critical challenge in civil engineering: the inefficient and inaccurate manual inspection of large-scale underwater concrete structures. By integrating deep learning with robotics, Zhu’s 2024 study (8 citations) offers a transformative, automated solution for detecting structural damage in submerged environments, significantly enhancing safety and operational efficiency. In parallel, Zhu has advanced optimization theory with the development of the SAGWO algorithm (6 citations), an adaptive grey wolf optimizer that introduces a second-order high-pass filter-based transfer function to improve convergence speed and solution accuracy. This innovation overcomes fundamental limitations of the standard Grey Wolf Optimizer, demonstrating Zhu’s ability to refine nature-inspired algorithms for complex, real-world problems. With a growing citation impact and a focus on bridging theoretical optimization with practical engineering applications, Zhu’s work is shaping the future of autonomous inspection systems and intelligent computational methods.
Research Focus
Key Achievements
Top Papers
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