SIMSLab
The winning research project ‘Smart Noise Logger‐based Leak Diagnosis System’ will explore a new approach to interpret noise logger outputs and pin point the leak location without the need for other technologies, such as correlators or acoustic. It is a key step in improving the Hong Kong Water Distribution Network and contributing to a better understanding of pipe performance. The main users of the proposed research will be academics, contractors, municipal personnel, and other interested stakeholders.
The winning research project supports the technological development of MiC projects that mitigates various uncertainties associated with the implementation of MiC project in Hong Kong, such as inclement weather. It also explores the optimum construction producers for various building shapes and number of floors in Hong Kong.
The winning research project aims to develop a closed-circuit-television (CCTV)-based smart/sustainable pipeline management system (SPMS) for sewers. The research will identify/study the critical factors/defects that affect sewer conditions in Hong Kong. The Drainage Services Department (DSD) database will be studied to complete missing data and design a GIS based data model for sewers. Finally, the SPMS will be developed using Machine/Deep Learning and Artificial Intelligence techniques to assist in intervention plans and budget allocation. It will be tested using data collected from a sample of 30 km of sewers recommended by DSD. The proposed approach, which is in line with the government priorities, will help DSD identify severe sewer deficiencies and focus limited funding on those most qualified. It is deemed essential for the government in Hong Kong to meet the public needs of welfare, safety, sustainability, and health.
SIMSLab