Program 4

Analytics and machine learning for carbon performance

Program 4 focuses on leveraging analytics and machine learning to enhance carbon performance across the infrastructure lifecycle. This program aims to address the challenges of processing and interpreting complex, uncertain, and noisy emission data through innovative projects. Key initiatives include developing federated learning systems for accurate carbon performance modelling without data sharing, creating risk-carbon-cost optimized maintenance strategies using AI for sustainable operation, and devising explainable visualization tools for real-time infrastructure performance monitoring. These projects are designed to provide a comprehensive toolkit for managing carbon emissions effectively, ensuring long-term carbon neutrality and supporting the infrastructure sector’s transition towards more sustainable practices. Through advanced analytics and machine learning, Program 4 is set to revolutionize how we understand and improve carbon performance in infrastructure development.

Co-Lead:

Professor Lihai Zhang

Project 1

Developing federated learning with AI on the edge system for data mining, processing and carbon performance modelling​

Project 2

Developing risk-carbon-cost optimised maintenance strategy

Project 3

Developing explainable visualisation tools for real-time performance monitoring and management