Overview
Chengzhang is currently a PhD candidate at the BIM for Smart Engineering Centre. He previously obtained a first-class MSc in Civil Engineering from the University of Southampton and holds a dual major in Computer Science & Civil Engineering from his undergraduate studies.
As an interdisciplinary researcher, he is committed to developing AI algorithms specific to the engineering field.
Research Interests:
- Computer Vision & Deep Learning
- Multimodal Data Fusion for Damage Detection
- AI for Structure Health Monitoring
Publication
2024
- Gao, Y., Li, H., Fu, W., Chai, C. and Su, T. 2024. Damage volumetric assessment and digital twin synchronization based on LiDAR point clouds. Automation in Construction 157, article number: 105168. (10.1016/j.autcon.2023.105168)
- Chai, C., Gao, Y., Li, H. and Zhu, X. 2024. Corrosion SAM: adapting segment anything model with parameter-efficient fine-tuning for structural corrosion inspection. Presented at: 31st EG-ICE International Workshop on Intelligent Computing in Engineering, Vigo, Spain, 3-5 July 2024.
- Chen, K., Chai, C. and Li, H. 2024. Universal decision-making system for life cycle maintenance of bridge based on deep reinforcement learning. Presented at: 31st EG-ICE International Workshop on Intelligent Computing in Engineering, Vigo, Spain, 3-5 July 2024.
- Liu, J., Chai, C., Li, H., Gao, Y. and Zhu, X. 2024. LLM-informed drone visual inspection of infrastructure. Presented at: 31st EG-ICE International Workshop on Intelligent Computing in Engineering, Vigo, Spain, 3-5 July 2024.
- Chai, C., Gao, Y., Li, H. and Xiong, G. 2024. Automatic generation of bridge defect descriptions using image captioning techniques. Presented at: The 10th International Conference on Construction Engineering and Project Management, Sapporo, Hokkaido, Japan, 29 July - 01 August 2024ICCEPM 2024 Conference Proceedings. pp. 319-326.
2023
- Gao, Y., Chai, C., Li, H. and Fu, W. 2023. A deep learning framework for intelligent fault diagnosis using AutoML-CNN and image-like data fusion. Machines 11(10), article number: 932. (10.3390/machines11100932)
Cynadleddau
- Chai, C., Gao, Y., Li, H. and Zhu, X. 2024. Corrosion SAM: adapting segment anything model with parameter-efficient fine-tuning for structural corrosion inspection. Presented at: 31st EG-ICE International Workshop on Intelligent Computing in Engineering, Vigo, Spain, 3-5 July 2024.
- Chen, K., Chai, C. and Li, H. 2024. Universal decision-making system for life cycle maintenance of bridge based on deep reinforcement learning. Presented at: 31st EG-ICE International Workshop on Intelligent Computing in Engineering, Vigo, Spain, 3-5 July 2024.
- Liu, J., Chai, C., Li, H., Gao, Y. and Zhu, X. 2024. LLM-informed drone visual inspection of infrastructure. Presented at: 31st EG-ICE International Workshop on Intelligent Computing in Engineering, Vigo, Spain, 3-5 July 2024.
- Chai, C., Gao, Y., Li, H. and Xiong, G. 2024. Automatic generation of bridge defect descriptions using image captioning techniques. Presented at: The 10th International Conference on Construction Engineering and Project Management, Sapporo, Hokkaido, Japan, 29 July - 01 August 2024ICCEPM 2024 Conference Proceedings. pp. 319-326.
Erthyglau
- Gao, Y., Li, H., Fu, W., Chai, C. and Su, T. 2024. Damage volumetric assessment and digital twin synchronization based on LiDAR point clouds. Automation in Construction 157, article number: 105168. (10.1016/j.autcon.2023.105168)
- Gao, Y., Chai, C., Li, H. and Fu, W. 2023. A deep learning framework for intelligent fault diagnosis using AutoML-CNN and image-like data fusion. Machines 11(10), article number: 932. (10.3390/machines11100932)
- Gao, Y., Li, H., Fu, W., Chai, C. and Su, T. 2024. Damage volumetric assessment and digital twin synchronization based on LiDAR point clouds. Automation in Construction 157, article number: 105168. (10.1016/j.autcon.2023.105168)