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PhD
Research Assistant Professor
Tel.: (852) 2766 5797
E-mail address : yan-tao.yu@polyu.edu.hk


Dr. Yantao YU is a research assistant professor of Construction Technology in the Department of Building and Real Estate (BRE) at The Hong Kong Polytechnic University (PolyU). She received her doctoral degree in construction informatics from BRE at PolyU in July 2020, and her master’s and bachelor’s degree in construction management from Tsinghua University in 2014 and 2017, respectively. Her research interest is construction informatics and automation. Dr.Yu was the recipient of the Hong Kong PhD Fellowship in 2017, and the best paper awardee of the 35th International Symposium on Automation and Robotics in Construction.


Area of Research

  • Information Management for Construction and Real Estate
  • Construction for Better Living

Research Interests

  • Smart construction technologies
  • Construction robotics
  • Occupational safety and health

Professional Service

  • Guest Editor, Advanced Engineering Informatics, special issue on Robotics in the Construction Industry
  • Reviewer, Automation in Construction
  • Reviewer, Computer and Education
  • Reviewer, IEEE Access
  • Reviewer, IEEE Industrial Electronic Magazine
  • Reviewer, Journal of Construction Engineering and Management
  • Reviewer, Reliability Engineering and System Safety
  • Reviewer, Sustainable Energy Technologies and Assessments

Publication List

  1. Yu, Y. *, Li, H., Cao,J., & Luo, X. Three-dimensional working pose estimation in industrial scenarios with monocular camera. IEEE Internet of Things Journal. DOI: 10.1109/JIOT.2020.3014930. (IF: 9.515) (ahead-of-print)
  2. Yu, Y., Li, H., Yang, X., Kong, L., Luo, X., & Wong, A. Y. L. (2019). An automatic and non-invasive physical fatigue assessment method for construction workers. Automation in Construction, 103, 1–12. DOI: 10.1016/j.autcon.2019.02.020. (IF: 5.669)
  3. Yu, Y., Guo, H., Ding, Q., Li, H., & Skitmore, M. (2017). An experimental study of real-time identification of construction workers’ unsafe behaviors. Automation in Construction, 82, 193–206. DOI: 10.1016/j.autcon.2017.05.002. (IF: 5.669)
  4. Yu, Y., Li, H., Umer, W., Dong, C., Yang, X., Skitmore, M., & Wong, A. Y. L. (2019). Automatic Biomechanical Workload Estimation for Construction Workers by Computer Vision and Smart Insoles. Journal of Computing in Civil Engineering, 33(3), 04019010. DOI: 10.1061/(ASCE)CP.1943-5487.0000827. (IF: 2.979)
  5. Yu, Y., Yang, X., Li, H., Luo, X., Guo, H., & Fang, Q. (2019). Joint-Level Vision-Based Ergonomic Assessment Tool for Construction Workers. Journal of Construction Engineering and Management, 145(5), 04019025. DOI: 10.1061/(ASCE)CO.1943-7862.0001647. (IF: 2.347)
  6. Antwi-Afari, M. F., Li, H., Umer, W., Yu, Y. *, & Xing, X. (2020). Construction Activity Recognition and Ergonomic Risk Assessment Using a Wearable Insole Pressure System. Journal of Construction Engineering and Management, 146(7), 04020077. DOI: 10.1061/(ASCE)CO.1943-7862.0001849. (IF: 2.347)
  7. Kong, L., Li, H., Yu, Y.*, Luo, H., Skitmore, M., & Antwi-Afari, M. F. (2018). Quantifying the physical intensity of construction workers, a mechanical energy approach. Advanced Engineering Informatics, 38, 404–419. DOI: 10.1016/j.aei.2018.08.005. (IF: 3.879)
  8. Guo, H., Yu, Y., Ding, Q., & Skitmore, M. (2018). Image-and-Skeleton-Based Parameterized Approach to Real-Time Identification of Construction Workers’ Unsafe Behaviors. Journal of Construction Engineering and Management, 144(6), 04018042. DOI: 10.1061/(ASCE)CO.1943-7862.0001497
  9. Guo, H., Yu, Y., & Skitmore, M. (2017). Visualization technology-based construction safety management: A review. Automation in Construction, 73, 135–144. DOI: 10.1016/j.autcon.2016.10.004
  10. Guo, H., Yu, Y., Xiang, T., Li, H., & Zhang, D. (2017). The availability of wearable-device-based physical data for the measurement of construction workers’ psychological status on site: From the perspective of safety management. Automation in Construction, 82, 207–217. DOI: 10.1016/j.autcon.2017.06.001
  11. Yang, X., Yu, Y., Li, H., Luo, X., & Wang, F. (2017). Motion-based analysis for construction workers using biomechanical methods. Frontiers of Engineering Management, 4(1), 84. DOI: 10.15302/J-FEM-2017004
  12. Luo, X., Li, H., Yu, Y., Zhou, C., & Cao, D. (2020). Combining deep features and activity context to improve recognition of activities of workers in groups. Computer-Aided Civil and Infrastructure Engineering, mice.12538. DOI: 10.1111/mice.12538
  13. Umer, W., Li, H., Yu, Yu., Antwi-Afari, M. F., Anwer, S., & Luo, X. (2020). Physical exertion modeling for construction tasks using combined cardiorespiratory and thermoregulatory measures. Automation in Construction, 112, 103079. DOI: 10.1016/j.autcon.2020.103079
  14. Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., & Yang, X. (2018). Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network. Computer-Aided Civil and Infrastructure Engineering, 33(12), 1090–1109. DOI: 10.1111/mice.12412
  15. Antwi-Afari, M. F., Li, H., Yu, Y., & Kong, L. (2018). Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers. Automation in Construction, 96, 433–441. DOI: 10.1016/j.autcon.2018.10.004
  16. Luo, X., Li, H., Cao, D., Yu, Y., Yang, X., & Huang, T. (2018). Towards efficient and objective work sampling: Recognizing workers’ activities in site surveillance videos with two-stream convolutional networks. Automation in Construction, 94, 360–370. DOI: 10.1016/j.autcon.2018.07.011
  17. Luo, X., Li, H., Yang, X., Yu, Y., & Cao, D. (2018). Capturing and Understanding Workers’ Activities in Far-field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning. Computer-Aided Civil and Infrastructure Engineering. DOI: 10.1111/mice.12419
  18. Fang, Q., Li, H., Luo, X., Ding, L., Rose, T. M., An, W., & Yu, Y. (2018). A deep learning-based method for detecting non-certified work on construction sites. Advanced Engineering Informatics, 35, 56–68. DOI: 10.1016/j.aei.2018.01.001
  19. Guo, H., Yu, Y., Liu, W., & Zhang, W. (2014). Integrated application of BIM and RFID in construction safety management. Journal of Engineering Management, (4). DOI: 10.13991/j.cnki.jem.2014.04.018

Conference Proceedings

  1. Yu, Y., Li, H., & Yang, X. (2019). 3D Posture Estimation from 2D Posture Data for Construction Workers. In M. Al-Hussein (Ed.), 2019 Proceedings of the 36th ISARC (pp. 26–34). Banff, AB, Canada: The International Association for Automation and Robotics in Construction. DOI: 10.22260/ISARC2019/0004
  2. Yu, Y., Li, H., & Wong, Y. L. A. (2019). A non-intrusive method for measuring construction workers’ muscle fatigue. In T. Linner & T. Bock (Eds.), Proceedings of the CIB W119 workshop on Automation and Robotics in Construction (pp. 26–36). Hong Kong: International Council for Resaerch and Innovation in Building and Construction. DOI: 10.14459/2019md1519198
  3. Yu, Y., Li, H., Yang, X., & Umer, W. (2018). Estimating Construction Workers’ Physical Workload by Fusing Computer Vision and Smart Insole Technologies. In 2018 Proceedings of the 35th ISARC (pp. 1212–1219). Berlin: International Association for Automation and Robotics in Construction. DOI: 10.22260/ISARC2018/0168
  4. Yu, Y., Zhang, J., & Guo, H. (2017). Investigation of the Relationship between Construction Workers’ Psychological States and Their Unsafe Behaviors Using Virtual Environment-Based Testing. In Computing in Civil Engineering 2017 (pp. 417–424). Reston, VA: American Society of Civil Engineers. DOI: 10.1061/9780784480847.052
  5. Yang, X., Wang, F., Li, H., Yu, Y., Luo, X., & Zhai, X. (2019). A Low-Cost and Smart IMU Tool for Tracking Construction Activities. In M. Al-Hussein (Ed.), 2019 Proceedings of the 36th ISARC (pp. 35–41). Banff, Alberta, Canada: The International Association for Automation and Robotics in Construction. DOI: 10.22260/ISARC2019/0005

Book Chapters

  1. Guo, H., Liu, W., Zhang, W., Yu, Y. “BIM-based Construction Safety Management Platform.” In: BIM and Construction Safety Management (pp. 69–91), edited by X. Zhao, China Architecture and Building Press, ISBN: 9787112206070