About SIMS Lab

Smart Infrastructure Management Systems (SIMS) is a research lab providing state-of-the-art solutions for sustainable management of infrastructure assets. SIMS lab is one of the leading research lab in Hong Kong partnering with industry and academia to solve the world's most complex challenges in infrastructure management to help build sustainable future.

🚀 Call for Papers – Special Issue on Construction Project Management and Infrastructure Sustainability 🌱

Guest Editors: Prof. Tarek Zayed, Dr. Sherif Abdelkhalek, Dr. Mohamed Hussein, Dr. Eslam Ali, Dr. Abdul Hannan Qureshi

We invite researchers and practitioners to contribute original research, case studies, and reviews on sustainable infrastructure, digital technologies (BIM, Digital Twin, IoT), AI-driven monitoring, climate-resilient design, circular economy, and more.

Key Themes: Sustainable construction management, BIM/GIS integration, AI/IoT applications, predictive maintenance, climate-resilient infrastructure, green materials, carbon neutrality, and lifecycle assessment.

📅 Deadlines: Abstract – 20 June 2026 | Manuscript – 20 August 2026
📖 Journals: Buildings, CivilEng, Infrastructures, Safety, Sustainability, Systems
🔗 Submit here: https://www.mdpi.com/topics/1421Y64JAB

Special Issue on Sustainable Infrastructure

Research

Our research interest includes but not limited to infrastructure management, simulation and artificial intelligent applications in construction, asset performance, scheduling, life cycle cost (LCC) analysis, budget allocation, and risk assessment for construction and rehabilitation of highways, oil and gas pipelines, water and sewer systems, buildings, bridges and tunnels...more.

Publications

Selected 2025 Publications:

[J1] Fares, A., Zayed, T., Faris, N., Yussif, A.M., & Abdelkhalek, S. (2025). Pavement thickness evaluation using GPR and fuzzy logic. Automation in Construction, 175, July, 106236. https://doi.org/10.1016/j.autcon.2025.106236

[J2] Taiwo, R., Shaban, I. A., Ahmad, T. & Zayed, T. (2025). Big data-driven prediction of watermain failures in semi-tropical regions: Case study of Hong Kong's distribution network. Automation in Construction, 175, July, 106159. https://doi.org/10.1016/j.autcon.2025.106159

[J3] Arshad, H., Zayed, T., Bakhtawar, B., Chen, A., & Li, H. (2025). Damage assessment of modular integrated construction during transport and assembly using a hybrid CNN–Gated recurrent unit model. Automation in Construction, 174, June, 106136. https://doi.org/10.1016/j.autcon.2025.106136

[J4] Qureshi, A. H., Zayed, T., Ibrahim, A., Xiong, C., Abdelkhalek, S., & Manzoor, B. (2025). Optimum thermal gradient thresholds (OTGT) system for inspecting bridges using Infrared Thermography (IRT). Advanced Engineering Informatics, 67, June, 103520. https://doi.org/10.1016/j.aei.2025.103520

[J5] Taiwo, R., Zayed, T., & Adey, B. T. (2025). A novel ensemble learning framework for predicting the causes of water pipe failures. Reliability Engineering and System Safety, 264, June, 111320. https://doi.org/10.1016/j.ress.2025.111320

[J6] Xing, J., Ali, E., Zayed, T., Elshaboury, N., Eltoukhy, A. E., Abdelkader, E. M., & Taiwo, R. (2025). Revealing critical pipes in water networks through integrated edge centrality and multi-criteria vulnerability analysis. Water Research X, June, 100366. https://doi.org/10.1016/j.wroa.2025.100366

[J7] Assaf, M., Zayed, T., Hussein, M., & Alfalah, G. (2025). Blockchain-enabled cash flow management system in offsite construction projects. Results in Engineering, 27, June, 105508. https://doi.org/10.1016/j.rineng.2025.105508

SIMS Lab

PolyU Department of Building and Real Estate