Averting Disaster: New Ways to Assess Bushfire Risk and Building Integrity. This project aims to develop a new method of assessing bushfire risk and building integrity using drone-based advanced technologies and computational fluid dynamics based heat transfer modelling for buildings located in bushfire prone areas. This coupled approach will enable the evaluation of bushfire effects on buildings and provide pre-bushfire condition/risk assessments, and site-specific cost-effective remedial actio ....Averting Disaster: New Ways to Assess Bushfire Risk and Building Integrity. This project aims to develop a new method of assessing bushfire risk and building integrity using drone-based advanced technologies and computational fluid dynamics based heat transfer modelling for buildings located in bushfire prone areas. This coupled approach will enable the evaluation of bushfire effects on buildings and provide pre-bushfire condition/risk assessments, and site-specific cost-effective remedial actions to reduce or eliminate bushfire damage and mitigate the risks pre-bushfire season. The new method will be applied to three selected buildings through which further enhancements and validations can be achieved. This project will showcase how the selected buildings and their components can be made bushfire safe.Read moreRead less
Revolutionising water-quality monitoring in the information age. In today’s information age, automated low-cost sensors distributed in the environment have the potential to revolutionise the way we monitor and manage air, water and soil. This project aims to develop novel statistical methods to detect anomalies in the data generated from these in-situ sensors with computationally efficient modelling on river networks through space and time, with the applied goals of automating anomaly detection ....Revolutionising water-quality monitoring in the information age. In today’s information age, automated low-cost sensors distributed in the environment have the potential to revolutionise the way we monitor and manage air, water and soil. This project aims to develop novel statistical methods to detect anomalies in the data generated from these in-situ sensors with computationally efficient modelling on river networks through space and time, with the applied goals of automating anomaly detection in water-quality data and generating predictions of sediment and nutrient concentrations throughout river networks in near-real time. This will represent a fundamental increase in scientific knowledge, which will be immediately useful in the domains of aquatic science, environmental monitoring, and statistics.Read moreRead less