Time-dependent dynamic, creep and shrinkage response of curved structural members. This project concerns curved structural members, such as bridge beams, that are subjected to dynamic excitation and to concrete shrinkage and creep. Hitherto, unified formulations for the structural behaviour of these members have not been properly developed. The proposal seeks to build on a previous ARC DP of the investigator that produces significant results for static loading, by developing a sophisticated meth ....Time-dependent dynamic, creep and shrinkage response of curved structural members. This project concerns curved structural members, such as bridge beams, that are subjected to dynamic excitation and to concrete shrinkage and creep. Hitherto, unified formulations for the structural behaviour of these members have not been properly developed. The proposal seeks to build on a previous ARC DP of the investigator that produces significant results for static loading, by developing a sophisticated methodology to handle non-static dynamic loading and for shrinkage and creep. It will develop advanced mathematical tools to enable the safe and efficient design of a multiplicity of structures that is of benefit to on and offshore Australian technology.Read moreRead less
Development of a Local Spectral Method for the Computations of Thin-Walled Structures. This project will benefit Aust. society by providing a powerful tool for improving the safe and cost effective design of structures under extreme conditions (high frequency vibration, complicating supporting conditions). The method has the potential to be further developed to provide solutions to unsolved problems in acoustic wave transport, short electromagnetic wave propagation etc. The research training of ....Development of a Local Spectral Method for the Computations of Thin-Walled Structures. This project will benefit Aust. society by providing a powerful tool for improving the safe and cost effective design of structures under extreme conditions (high frequency vibration, complicating supporting conditions). The method has the potential to be further developed to provide solutions to unsolved problems in acoustic wave transport, short electromagnetic wave propagation etc. The research training of the project will help to keep Australia to be at the forefront in this research field and the published research findings will promote the reputation of Australian researchers in the field of computational engineering. The international collaboration will be strengthened between the Investigator's team and his colleagues in US. Read moreRead less
Material properties and mechanical behaviours of carbon nanotube-reinforced composite structures. Polymer nanocomposites and their applications in advanced structures represent one of the most significant developments of composite materials and structures in the past decade. This project aims to quantify the equivalent material properties of effective individual carbon nanotube in polymer matrix, predict the mechanical properties of carbon nanotube reinforced polymer composites and optimise the ....Material properties and mechanical behaviours of carbon nanotube-reinforced composite structures. Polymer nanocomposites and their applications in advanced structures represent one of the most significant developments of composite materials and structures in the past decade. This project aims to quantify the equivalent material properties of effective individual carbon nanotube in polymer matrix, predict the mechanical properties of carbon nanotube reinforced polymer composites and optimise the mechanical behaviours of functionally graded carbon nanotube polymer composite structures through a multi-scale modelling and analytical approach. It will establish guidelines and strategies for design and development of high performance carbon nanotube-reinforced polymer composites and their functionally graded structures. Read moreRead less
Condition-Based Maintenance Optimisation for Queensland’s Railways. Rail maintainers currently use time-based (scheduled) approaches to balance the costs and benefits of inspections and maintenance. Changing to condition-based maintenance has the potential to reduce costs and improve track condition. This project aims to enable this approach for rail by developing: 1) new track degradation prediction techniques combining Big Data and engineering knowledge; 2) new on-board sensing capabilities fo ....Condition-Based Maintenance Optimisation for Queensland’s Railways. Rail maintainers currently use time-based (scheduled) approaches to balance the costs and benefits of inspections and maintenance. Changing to condition-based maintenance has the potential to reduce costs and improve track condition. This project aims to enable this approach for rail by developing: 1) new track degradation prediction techniques combining Big Data and engineering knowledge; 2) new on-board sensing capabilities for frequent, low-cost track monitoring; 3) novel inspection and maintenance optimisation methods to efficiently allocate resources. The knowledge generated by this project is expected to decrease maintenance costs, safety risk, and track closures and therefore enhance the affordability and reliability of rail travel.Read moreRead less
A novel physical-digital approach for the assessing a large critical asset. This project aims to deliver an artificial intelligence-enabled decision-making tool to maintain and manage the floating covers of vast lagoons that treat raw sewage. The cover harvests the biogas released from the anaerobic digestion of sewage for electric power generation that exceeds the plant’s requirement. The approach involves an innovative thermographic technique and exploits transfer learning to adapt neural netw ....A novel physical-digital approach for the assessing a large critical asset. This project aims to deliver an artificial intelligence-enabled decision-making tool to maintain and manage the floating covers of vast lagoons that treat raw sewage. The cover harvests the biogas released from the anaerobic digestion of sewage for electric power generation that exceeds the plant’s requirement. The approach involves an innovative thermographic technique and exploits transfer learning to adapt neural networks trained on lab-scale and synthetic data to field implementation. The outcome is a machine learning framework to optimise biogas harvesting and renewable energy generation, and to avoid structural failure, that is capable of continuous improvement to take into account improved data and/or modelling capabilities.Read moreRead less