SIMSLab

SIMS Lab

PolyU Department of Building and Real Estate

Smart and Sustainable Drainage Network (SSDN) in Hong Kong using Artificial Intelligence and Machine Learning techniques  

Project Publications

  1. Abdelkhalek, S., & Zayed, T. (2023). A multi-tier deterioration assessment models for sewer and stormwater pipelines in Hong Kong. Journal of Environmental Management, 345, 118913.
  2. Alshami, A., Elsayed, M., Mohandes, S. R., Kineber, A. F., Zayed, T., Alyanbaawi, A., & Hamed, M. M. (2022). Performance assessment of sewer networks under different blockage situations using Internet-of-Things-based technologies. Sustainability, 14(21), 14036.
  3. Mohandes, S. R., Kaddoura, K., Singh, A. K., Elsayed, M. Y., Banihashemi, S., Antwi-Afari, M. F., ... & Zayed, T. (2024). Application of a hybrid fuzzy-based algorithm to investigate the environmental impact of sewer overflow. Smart and Sustainable Built Environment.
  4. Alshami, A., Elsayed, M., Ali, E., Eltoukhy, A. E., & Zayed, T. (2023). Monitoring blockage and overflow events in small-sized sewer network using contactless flow sensors in Hong Kong: Problems, causes, and proposed solution. IEEE access, 11, 87131-87149.
  5. Sojobi, A. O., & Zayed, T. (2022). Impact of sewer overflow on public health: A comprehensive scientometric analysis and systematic review. Environmental research, 203, 111609.
  6. Owolabi, T. A., Mohandes, S. R., & Zayed, T. (2022). Investigating the impact of sewer overflow on the environment: A comprehensive literature review paper. Journal of Environmental Management, 301, 113810.

SIMSLab