Methodology for assessing the vulnerability of multimodal transport networks and developing remedial measures to safeguard network performance. When transport networks fail, the effects on people and the economy can be devastating. The consequences for Hobart of the 1975 Tasman Bridge collapse provide a prime example. Failure may also result from extreme weather and natural disasters, traffic congestion and incidents, commercial failure, human error, or malevolence (such as sabotage). This proje ....Methodology for assessing the vulnerability of multimodal transport networks and developing remedial measures to safeguard network performance. When transport networks fail, the effects on people and the economy can be devastating. The consequences for Hobart of the 1975 Tasman Bridge collapse provide a prime example. Failure may also result from extreme weather and natural disasters, traffic congestion and incidents, commercial failure, human error, or malevolence (such as sabotage). This project will develop a methodology for auditing a transport network to identify where infrastructure failure will have the worst consequences for movement of people and goods. The research will provide tools for planners to determine critical network locations, and devise strategies and remedial measures to safeguard network performance.Read moreRead less
Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provi ....Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provide real-time decisions for plant operators on the required treatment regime for incoming raw water, and advise them on the optimal reservoir offtake depth. This will potentially minimise treatment costs and health risks for consumers. The ultimate goal is to significantly enhance current water supply management practices.Read moreRead less