Hydrogen carbon waste into concrete: AI assisted nanoscience approach. The carbon waste from hydrogen production will be converted into carbon nanosheets on abundant construction materials for the creation of stronger and more durable concrete. Cutting-edge nanoscience-based experiments, as well as sophisticated modelling techniques including machine learning and finite element modelling, will be employed. The findings will drive advances in clean hydrogen production, carbon waste utilisation, c ....Hydrogen carbon waste into concrete: AI assisted nanoscience approach. The carbon waste from hydrogen production will be converted into carbon nanosheets on abundant construction materials for the creation of stronger and more durable concrete. Cutting-edge nanoscience-based experiments, as well as sophisticated modelling techniques including machine learning and finite element modelling, will be employed. The findings will drive advances in clean hydrogen production, carbon waste utilisation, cement hydration, nanotechnology and concrete technology for the next generation of an upskilled workforce and the promotion of a circular economy. This project will be carried out in collaboration with Australian and international renowned experts in computational modelling, nanomaterials and concrete materials.Read moreRead less
Asset Intelligence: Maximising Operational Effectiveness for Digital Era . The primary aim of this project is to develop an innovative lifecycle semantic–based decision making approach through asset intelligence so as to maximize the operational effectiveness maintenance, repair and rehabilitation planning of infrastructure assets, such as concrete pavement. The research intends to address an important gap by providing logical formalisms and real-time capability to life-cycle asset information t ....Asset Intelligence: Maximising Operational Effectiveness for Digital Era . The primary aim of this project is to develop an innovative lifecycle semantic–based decision making approach through asset intelligence so as to maximize the operational effectiveness maintenance, repair and rehabilitation planning of infrastructure assets, such as concrete pavement. The research intends to address an important gap by providing logical formalisms and real-time capability to life-cycle asset information through computational intelligence. The expected outcome will be an intelligent asset management platform that provides structured and semantically enriched lifecycle asset information for optimised solutions to help reduce the cost, time and effort in asset information storage and retrieval, and decision-making. Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC150100023
Funder
Australian Research Council
Funding Amount
$4,000,000.00
Summary
ARC Training Centre for Advanced Manufacturing of Prefabricated Housing. ARC Training Centre for Advanced Manufacturing of Prefabricated Housing. This training centre aims to unlock the potential for growth of Australia’s prefabricated building industry by creating a sustainable training ecosystem including both industry and universities. It seeks to enable the next generation of engineers and architects to apply advanced manufacturing principles to prefabricated modular buildings. This emerging ....ARC Training Centre for Advanced Manufacturing of Prefabricated Housing. ARC Training Centre for Advanced Manufacturing of Prefabricated Housing. This training centre aims to unlock the potential for growth of Australia’s prefabricated building industry by creating a sustainable training ecosystem including both industry and universities. It seeks to enable the next generation of engineers and architects to apply advanced manufacturing principles to prefabricated modular buildings. This emerging highly trained workforce, driven by the needs of the customer, should identify innovations in the use of advanced materials, designs for manufacturing, and assembly. The centre aims to secure a competitive advantage for Australia in the global value chain leading to local employment growth and increased exports of prefabricated products and services.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
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
Inerter-enhanced metastructure for structural vibration control. Meta-based technique has been proposed for vibration control recently due to its special wave filtering effect. However, the current techniques are difficult to attenuate low-frequency waves, thus not suitable for civil structural vibration control. This project proposes incorporating an inerter-based element into the unit cell of a metastructure. Due to the unique mass amplification characteristic of inerter element, manipulating ....Inerter-enhanced metastructure for structural vibration control. Meta-based technique has been proposed for vibration control recently due to its special wave filtering effect. However, the current techniques are difficult to attenuate low-frequency waves, thus not suitable for civil structural vibration control. This project proposes incorporating an inerter-based element into the unit cell of a metastructure. Due to the unique mass amplification characteristic of inerter element, manipulating low-frequency waves becomes possible. Practical designs are developed and applied to control the adverse vibrations of engineering structures induced by three typical vibration sources. Comprehensive analytical, experimental and numerical studies are carried out to examine the effectiveness of the proposed method.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170100119
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Unlocking the changing strength of fine-grained soils in numerical analyses. This project aims to numerically simulate strain-softening-hardening in fine-grained soils. Fine-grained soils soften during deformation and harden as excess pore pressures dissipate. Models exist that allow strain-softening and hardening in finite element simulations, but suffer from mesh-dependency. Regularisation methods can alleviate mesh-dependency, but an appropriate characteristic length for the regularisation is ....Unlocking the changing strength of fine-grained soils in numerical analyses. This project aims to numerically simulate strain-softening-hardening in fine-grained soils. Fine-grained soils soften during deformation and harden as excess pore pressures dissipate. Models exist that allow strain-softening and hardening in finite element simulations, but suffer from mesh-dependency. Regularisation methods can alleviate mesh-dependency, but an appropriate characteristic length for the regularisation is needed and difficult to determine. This project will use image-based soil deformation measurement and aspects of the finite element method to determine appropriate regularisation techniques, characteristic lengths and constitutive relations. Reliably modelling strain-softening and hardening in finite element simulations is expected to reduce uncertainty in design and make civil infrastructure cheaper.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
Understanding vibratory piles in sand: installation and lateral response. This project aims to address uncertainties in the design of vibro-driven piles. This promising alternative to impact-driven piles offers faster installation and requires no noise mitigation. The project expects to generate new knowledge of the effect of the installation process in sand on in-service pile response by integrating findings from innovative experiments and numerical modelling. This is particularly important for ....Understanding vibratory piles in sand: installation and lateral response. This project aims to address uncertainties in the design of vibro-driven piles. This promising alternative to impact-driven piles offers faster installation and requires no noise mitigation. The project expects to generate new knowledge of the effect of the installation process in sand on in-service pile response by integrating findings from innovative experiments and numerical modelling. This is particularly important for highly sensitive structures such as offshore wind turbines, which provide a rapidly increasing share of global energy supply. Expected outcomes include practical recommendations for vibro-piles in sand. This should provide sizeable benefits by unlocking vibro-piles as a viable method to reduce offshore wind farm costs.Read moreRead less