Zeping Wu

National University of Defense Technology, Wuxi Institute of Technology

Papers

Total Citations

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About

Zeping Wu is a researcher whose work bridges computational intelligence and structural reliability engineering. Their primary research areas include reliability analysis, neural network optimization, and robotic 3D measurement. Wu’s most significant contribution is the development of an efficient reliability analysis method based on an improved radial basis function neural network (RBFNN), which addresses critical challenges in structural reliability—namely, low computational accuracy and efficiency. This work, published in 2023, has already garnered 12 citations, demonstrating its growing influence in the field. By enhancing the RBFNN’s performance, Wu’s method offers a practical tool for engineers to assess structural safety more reliably and quickly, reducing computational costs without sacrificing precision. Additionally, Wu has explored automated view planning for robot 3D measurement, a niche but impactful area for industrial automation. Though early in their career, Wu’s innovative approach to integrating neural networks with reliability analysis marks them as a promising contributor to computational mechanics and intelligent systems. Their work is particularly valuable for students and researchers seeking efficient, data-driven solutions to complex engineering challenges.