SIMS Lab · PolyU CMI
Research
We develop AI-driven sensing, diagnostics, and decision-support systems for the sustainable management of infrastructure — spanning water networks, drainage, modular construction, bridges, sewers, and pavements.
7Research areas
200+Publications
HK$25M+Research grants
25+Lab members
Active Research Areas
Smart Leak Detection System (SLDS)
AI-powered acoustic and hydraulic sensing for real-time detection and localisation of water leaks in urban distribution networks across Hong Kong.
Smart & Sustainable Drainage Network (SSDN)
Applying machine learning and digital-twin modelling to monitor, predict, and optimise the performance of stormwater and sewer drainage systems.
Modular Integrated Construction (MiC)
Advancing offsite and prefabricated construction through structural health monitoring, damage detection, and intelligent lifecycle management of modular units.
Water Pipe Failure Modelling & Analysis
Predictive modelling of watermain failures using big data, ensemble learning, and risk-based prioritisation to guide rehabilitation decisions and regulatory policy.
Smart Assessment of Bridge Deck Efficiency
Combining drone inspection, Ground-Penetrating Radar, and Infrared Thermography with machine learning to assess bridge deck condition and develop a quantified efficiency index.
Predicting Sewer Failure
Novel modelling frameworks that integrate structural, operational, and environmental factors to predict sewer pipe failure and optimise maintenance scheduling.
Smart Pavement & Sidewalks Management New
Developing AI-driven evaluation and condition assessment tools for pavement and sidewalk infrastructure, integrating GPR, fuzzy logic, and sensor fusion techniques.