UNIVERSITY OF HONG KONG
FACULTY OF ARCHITECTURE

Chen, Junjie 陳俊杰

BEng; MEng; PhD TJU; Aff.M.ASCE, MIEEE, MCHES

I am a postdoctoral fellow in the Department of Real Estate and Construction at HKU. I obtained my PhD in hydraulic engineering from Tianjin University in 2020, and was a visiting researcher at the University of Tennessee, Knoxville from 2018 to 2019. My research covers a broad area in construction informatics and built environment: Building Information Modeling (BIM), Computer Vision, Machine Learning, Intelligent Compaction, and building and infrastructure facility maintenance. I am particularly interested in exploiting the latent value of low-cost visual assets (dynamic video or static images; virtual renderings or photorealistic) in the AECO industry by the use of advanced machine learning techniques.

I am an affiliate member of the American Society of Civil Engineers (ASCE), a member of Institute of Electrical and Electronics Engineers (IEEE), and a member of the Chinese Hydraulic Engineering society (CHES).

Selected Publications

  • Junjie Chen, Weisheng Lu, Liang Yuan, Yijie We, and Fan Xue, Estimating Construction Waste Truck Payload Volume Using Monocular Vision, Resources, Conservation and Recycling 177 (2022) 106013.
  • Weisheng Lu, Junjie Chen, and Fan Xue, Using computer vision to recognize composition of construction waste mixtures: A semantic segmentation approach, Resources, Conservation and Recycling 178 (2022) 106022.
  • Junjie Chen, Shuai Li, Donghai Liu, and Weisheng Lu, Indoor Camera Pose Estimation via Style-Transfer 3D Models, Computer-Aided Civil and Infrastructure engineering (2021) 1–19.
  • Junjie Chen, Shuai Li, Weisheng Lu, Donghai Liu, Da Hu, and Maohong Tang. “Markerless Augmented Reality for Facility Management: Automated Spatial Registration based on Style Transfer Generative Network.” In 38th International Symposium on Automation and Robotics in Construction 2021, Dubai, UAE.
  • Chen, J., Lu, W., & Xue, F. (2021). “Looking beneath the surface”: A visual-physical feature hybrid approach for unattended gauging of construction waste composition. Journal of Environmental Management, 286, 112233.
  • Chen, J., & Liu, D (2020). Bottom-up image detection of water channel slope damages based on superpixel segmentation and support vector machine. Advanced Engineering Informatics, 47, 101205.

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