Fragmentation of rocks upon impact. The project aims to create a new understanding of how rocks fragment upon impact to allow more realistic predictions of rockfall hazards. Rockfall results in loss of human life, damage to infrastructure and economic loss; each year in Australia, millions of dollars are spent on rockfall protection. To mitigate rockfall risk, it is important to understand and predict how blocks break as they fall down a slope. Unfortunately, there is limited data and knowledge ....Fragmentation of rocks upon impact. The project aims to create a new understanding of how rocks fragment upon impact to allow more realistic predictions of rockfall hazards. Rockfall results in loss of human life, damage to infrastructure and economic loss; each year in Australia, millions of dollars are spent on rockfall protection. To mitigate rockfall risk, it is important to understand and predict how blocks break as they fall down a slope. Unfortunately, there is limited data and knowledge on this phenomenon. This project aims to produce a comprehensive, high-quality database of fragmentation events and develop an innovative fragmentation model that can be included in existing rockfall codes. This project is expected to lead to optimised and cost-effective rockfall barrier protection measures.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101293
Funder
Australian Research Council
Funding Amount
$426,717.00
Summary
Dynamic Fracturing and Energy Release Mechanisms in Heterogeneous Materials. The prediction of fracturing behaviour in geomaterials (i.e. rock, soil and concrete) under dynamic/impact loads is essential in dealing with a wide range of engineering problems including excavation and mining, blasting and fragmentation, earthquake engineering, impact cratering, and protective structure design However, current knowledge and modelling capabilities of these applications remains empirically based. This p ....Dynamic Fracturing and Energy Release Mechanisms in Heterogeneous Materials. The prediction of fracturing behaviour in geomaterials (i.e. rock, soil and concrete) under dynamic/impact loads is essential in dealing with a wide range of engineering problems including excavation and mining, blasting and fragmentation, earthquake engineering, impact cratering, and protective structure design However, current knowledge and modelling capabilities of these applications remains empirically based. This project aims to investigate fundamental issues governing the dynamic fracturing of geomaterials and apply this knowledge to advance the understanding and modelling capacity of dynamic fractures in geomaterials.Read moreRead less
Development of Intelligent Structures that can Self-evaluate Deterioration. This project aims to transform traditional civil structures into smart structures that can accurately identify current and future structural deterioration conditions and automatically notify the infrastructure management authority for timely maintenance. Civil structures deteriorate over their long life spans. Currently, we have no effective method to identify when deterioration has reached the point where maintenance is ....Development of Intelligent Structures that can Self-evaluate Deterioration. This project aims to transform traditional civil structures into smart structures that can accurately identify current and future structural deterioration conditions and automatically notify the infrastructure management authority for timely maintenance. Civil structures deteriorate over their long life spans. Currently, we have no effective method to identify when deterioration has reached the point where maintenance is required. The project plans to develop innovative structural deterioration evaluation systems using output-only vibration data and versatile optimisation algorithms to enable long-term deterioration assessment and maintenance management even under demanding operating conditions. These could be used with both conventional data acquisition systems and modern monitoring systems with smart wireless sensors. Expected project outcomes will enhance structural safety and maintenance efficiency.Read moreRead less
Innovative Data Driven Techniques for Structural Condition Monitoring . Safe and sustainable infrastructure involves the development and application of structural monitoring and assessment techniques for condition evaluation. This project develops an innovative structure condition monitoring approach based on the emerging digital technologies on image processing, data analytics and machine learning techniques, for better infrastructure asset management under operational environment. Expected out ....Innovative Data Driven Techniques for Structural Condition Monitoring . Safe and sustainable infrastructure involves the development and application of structural monitoring and assessment techniques for condition evaluation. This project develops an innovative structure condition monitoring approach based on the emerging digital technologies on image processing, data analytics and machine learning techniques, for better infrastructure asset management under operational environment. Expected outcomes of this project enhance the capacity to conduct the operational monitoring and data interpretation to deliver the best life cycle performance of infrastructure. This project should provide significant benefits to Australia in infrastructure asset management by reducing the interruption of infrastructure operations.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180101593
Funder
Australian Research Council
Funding Amount
$359,446.00
Summary
Seismic evaluation of non-structural unreinforced masonry components. This project aims to reduce earthquake risk posed by unreinforced masonry buildings. The project will use integrated experimental and numerical research to understand the dynamic interaction between timber floors, roofs and walls. New knowledge about this interaction will enable economical and safe earthquake design methods to be used for unreinforced masonry buildings.
Australian Laureate Fellowships - Grant ID: FL190100014
Funder
Australian Research Council
Funding Amount
$2,871,982.00
Summary
New Technologies for Delivering Sustainable Free-form Architecture. This project aims to harness the full potential of digital technologies to significantly enhance the performance and reduce the environmental impact of free-form architecture of the future. The research expects to establish a fundamentally new computational platform capable of producing diverse and competitive designs, and an environmentally friendly manufacturing process for realising such designs. Expected outcomes include an ....New Technologies for Delivering Sustainable Free-form Architecture. This project aims to harness the full potential of digital technologies to significantly enhance the performance and reduce the environmental impact of free-form architecture of the future. The research expects to establish a fundamentally new computational platform capable of producing diverse and competitive designs, and an environmentally friendly manufacturing process for realising such designs. Expected outcomes include an unprecedented cloud-based interactive design tool, and a novel minimum-waste manufacturing technology for fabricating mass-customised building components. This project will transform the architecture, engineering and construction (AEC) sector and make the Australian manufacturing industry more competitive globally.Read moreRead less
AI Assisted Probabilistic Structural Health Monitoring with Uncertain Data. This project aims to develop an advanced Artificial Intelligence (AI) assisted probabilistic structural health monitoring approach for civil engineering structures. The developed approach applies novel deep learning techniques with a large amount of data measured from uncertain and complex environment, for reliable structural condition monitoring and performance prediction. This project expects to make a step change in d ....AI Assisted Probabilistic Structural Health Monitoring with Uncertain Data. This project aims to develop an advanced Artificial Intelligence (AI) assisted probabilistic structural health monitoring approach for civil engineering structures. The developed approach applies novel deep learning techniques with a large amount of data measured from uncertain and complex environment, for reliable structural condition monitoring and performance prediction. This project expects to make a step change in data mining and interpretation. Expected outcomes of the project include novel AI assisted approaches to conduct probabilistic structural condition monitoring with sensitive features and future structural performance prediction. This will provide significant benefits to infrastructure asset owners to reduce maintenance costs.Read moreRead less
Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through dee ....Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through deep learning with structural conditions and load-carrying capacities obtained from vibration tests and finite element model analysis for efficient structural damage detection and quantification. The project will lead to effective structural health monitoring and enhance structural safety and reduce maintenance costs. Read moreRead less
A unified approach for estimating coastal flood risk. The project aims to develop a unified approach to quantifying flood risk. Because flooding is caused by multiple mechanisms such as extreme rainfall, storm surge and astronomical tide, accurately estimating flood levels in the Australian coastal zone is challenging. By quantifying flood risk in terms of these mechanisms, the project is expected to provide reliable flood risk estimates for both historical settings and future climate scenarios. ....A unified approach for estimating coastal flood risk. The project aims to develop a unified approach to quantifying flood risk. Because flooding is caused by multiple mechanisms such as extreme rainfall, storm surge and astronomical tide, accurately estimating flood levels in the Australian coastal zone is challenging. By quantifying flood risk in terms of these mechanisms, the project is expected to provide reliable flood risk estimates for both historical settings and future climate scenarios. The improved estimation should enable Australian water agencies and policy-makers to effectively design defence infrastructure (e.g. drainage systems) and urban planning policies to adapt to future flood risk.Read moreRead less
Optimal maintenance planning for critical mining and energy infrastructure. This project aims to develop cutting-edge mathematical algorithms for optimising maintenance activities in the mining and energy sectors. Such maintenance activities are prone to budget and time overruns due to poor planning - the result of outdated, inefficient manual processes. The project is expected to result in new maintenance planning methods, underpinned by rigorous mathematical theory, for reducing manual interve ....Optimal maintenance planning for critical mining and energy infrastructure. This project aims to develop cutting-edge mathematical algorithms for optimising maintenance activities in the mining and energy sectors. Such maintenance activities are prone to budget and time overruns due to poor planning - the result of outdated, inefficient manual processes. The project is expected to result in new maintenance planning methods, underpinned by rigorous mathematical theory, for reducing manual intervention and optimising both short- and long-term maintenance based on real-time sensor data. These new methods will be powerful tools for tackling the complexity of large-scale, time-critical maintenance projects, driving productivity in the resources industry and fostering collaboration between mathematicians and engineers.Read moreRead less