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Data61 is using data-driven techniques to improve prediction of pipe failures for water utilities. Intelligent predictions reduce maintenance costs, prioritise capital spend and minimise disruption to water supplies and the community.   The Challenge and Data61’s Approach Australian water utilities currently spend $1.4 billion per year on reactive repairs and maintenance, including the consequence cost […]

Critical Water Main Failure Prediction Data61 provides decision support for owners and maintainers of civil and industrial assets. Sensing, continuous monitoring and advanced data analysis techniques enable asset managers to make more informed maintenance decisions. The Challenge Assets are typically maintained when something goes wrong or according to preventative maintenance schedules. These approaches do not […]

“Data Analytics for Failure Prediction, Corrosion Estimation, & Operation Optimisation” Data61 is using machine learning based data analytics techniques to improve pipe failure prediction and reduce maintenance and operational costs. The “Intelligent Pipes” project aims to help water utilities prioritise capital spend and minimise disruption to water supplies and the community. Water Pipe Failure Prediction […]

Sulphide induced corrosion of concrete sewers is a serious problem in wastewater systems worldwide, particularly in countries with a warm climate like Australia. This project proposes a machine learning approach to predictive analysis for sewer corrosion based on Bayesian nonparametric methods. The proposed approach provides more modelling capacities and flexibilities for incorporating various factors (e.g., […]

Water demand analysis is crucial for water utilities to make a financially sustainable plan in terms of water pricing and infrastructure investments. It includes customer segmentation, influencing factor analysis and demand forecasting.  The proposed solution is based on advanced machine learning techniques. It considers both historical water consumption and influencing factors, e.g., water price, weather, […]