Development of prefabricated composite building panels and connections . This project will develop a new prefabricated composite brick-concrete panel technology, by exploiting cutting-edge manufacturing capabilities for the production of bricks and concrete components. It is expected to generate new robust design methodologies at both service and ultimate conditions by relying on advanced testing and theoretical modelling.
The project is expected to transform the current brick industry by repla ....Development of prefabricated composite building panels and connections . This project will develop a new prefabricated composite brick-concrete panel technology, by exploiting cutting-edge manufacturing capabilities for the production of bricks and concrete components. It is expected to generate new robust design methodologies at both service and ultimate conditions by relying on advanced testing and theoretical modelling.
The project is expected to transform the current brick industry by replacing traditional labour-intense brick construction with advanced and cost-effective prefabricated technologies that will enable brick construction to enter new markets, such as those of multi-storey buildings and complex load-bearing facades, previously not feasible or cost-effective with traditional brick technology.
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Structural assembly for remote housing using fibre reinforced composites. This project aims to address construction challenges in remote housing by off-site manufacturing and on-site assembly using fibre reinforced composites and digital made-to-measure approach. Its goal is to generate interdisciplinary knowledge and practical technologies for reliable, affordable and durable housing in remote harsh environments. Intended results include innovative connections and systems with valuable understa ....Structural assembly for remote housing using fibre reinforced composites. This project aims to address construction challenges in remote housing by off-site manufacturing and on-site assembly using fibre reinforced composites and digital made-to-measure approach. Its goal is to generate interdisciplinary knowledge and practical technologies for reliable, affordable and durable housing in remote harsh environments. Intended results include innovative connections and systems with valuable understanding of their performances under various loading scenarios and accurate digital visualization for remote construction. The outcomes expect to unlock remote development, enhance our competitive strengths for manufacturing and construction industries, and further offer new solutions in post-disaster recovery applications.Read moreRead less
Quantitative structural health assessment of large membrane-like structures. This project aims to develop a new approach, based on remote sensing and computational modelling, to assess and manage the structural health of large floating covers used for odour control and biogas harvesting to prevent unexpected failures. The project has potential benefits for high-value-added manufacturing and maintenance of these floating covers by Australian industry.
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