Ph.D student, The University of Hong Kong, Hong Kong.
šØāš¬ Haowei Zhang is a dynamic researcher specializing in structural health monitoring, concrete structure damage detection, and computer vision-based bridge Weight-In-Motion (WIM) systems. With a Ph.D. in progress at The University of Hong Kong, he has made significant contributions through cutting-edge research and impactful publications in top-tier journals. Haowei’s work spans deep learning, machine learning, and advanced imaging techniques for infrastructure health assessment, making him a standout researcher in civil engineering.
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Education š
Dr. Haowei Zhang is a current Ph.D. student in Civil Engineering at The University of Hong Kong, under the supervision of Prof. Ray Kai Leung Su. His doctoral research builds on his expertise in bridge safety performance and vehicle non-contact weigh-in-motion (WIM) technology. He holds a Masterās degree in Civil Engineering from Southeast University, where he focused on the safety performance of bridges, supervised by Prof. Gang Wu and Prof. Kang Gao. Prior to that, he earned his Bachelorās degree from Northeastern University in China, with a thesis on experimental building design supervised by Prof. Zhechao Wang. During his undergraduate studies, he also attended a summer training program at the University of Oxford, where he explored micromechanics and its applications in liquid metal 3D printing.
Experience š¼
Dr. Haowei Zhang is currently pursuing a Ph.D. in Civil Engineering at The University of Hong Kong, supervised by Prof. Ray Kai Leung Su. He holds a Masterās degree in Civil Engineering from Southeast University, where his research focused on vehicle non-contact weigh-in-motion (WIM) technology and the safety performance of bridges. Additionally, he earned his Bachelorās degree from Northeastern University, specializing in experimental building design.
Professionally, Dr. Zhang serves as a Junior Researcher at Dongqu Intelligent Transportation Infrastructure Technology (2023-present), where he contributes to the development of computer vision models and equipment for transportation infrastructure. He has also led research projects on bridge monitoring, concrete structure damage detection, and deep learning algorithms for weight identification. During his master’s studies, he worked as a part-time college psychological counselor at Southeast University, providing psychological support and managing data files for graduate students.
His work uniquely combines civil engineering, intelligent transportation systems, and mental health advocacy.
Research Interests š¬
Haowei Zhang’s research interests lie in structural health monitoring, computer vision-based WIM systems, deep learning, machine learning applications in civil engineering, and non-contact vehicle weight identification. His work focuses on developing innovative solutions for monitoring the integrity of concrete structures and enhancing safety through advanced image processing and data analysis.
Awards š
International Exhibition of Inventions of Geneva ā Silver Prize (2024)
Honor of Individual Academic Innovation ā Southeast University (2023)
First-Class Academic Scholarship ā Southeast University (2021)
Outstanding Undergraduate Student of Liaoning Province (2021)
National Scholarship (2020)
Publications Top Notes š
Automatic crack detection on concrete and asphalt surfaces using semantic segmentation network with hierarchical Transformer, Engineering Structures, 2023 Cited by: 45. link
Non-contact vehicle weight identification method based on explainable machine learning models and computer vision, Journal of Civil Structural Health Monitoring, 2023 Cited by: 20. link
Fully decouple convolutional network for damage detection of rebars in RC beams, Engineering Structures, 2023 Cited by: 25. link
A machine learning and game theory-based approach for predicting creep behavior of recycled aggregate concrete, Case Studies in Construction Materials, 2022 Cited by: 35. link
Efficient creep prediction of recycled aggregate concrete via machine learning algorithms, Construction and Building Materials, 2022 Cited by: 40. link