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Research Topic : Technology
Status : Active
Australian State/Territory : WA
Field of Research : Structural Engineering
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  • Active Funded Activity

    ARC Future Fellowships - Grant ID: FT210100050

    Funder
    Australian Research Council
    Funding Amount
    $873,160.00
    Summary
    Multi-hazard resilient hybrid modular structures. This project aims to develop the next generation of multi-hazard resilient modular construction methods for efficient, affordable and sustainable buildings. New demountable modular connections will be developed and the response of hybrid modular buildings to multiple hazards such as wind, earthquake, blast and impact will be investigated through a combination of experimental, numerical, and analytical studies. The project will develop knowledge o .... Multi-hazard resilient hybrid modular structures. This project aims to develop the next generation of multi-hazard resilient modular construction methods for efficient, affordable and sustainable buildings. New demountable modular connections will be developed and the response of hybrid modular buildings to multiple hazards such as wind, earthquake, blast and impact will be investigated through a combination of experimental, numerical, and analytical studies. The project will develop knowledge of the structural behaviour of hybrid modular buildings, and expects to deliver design methods and robust simplified models for building design purposes. This project will advance construction techniques and practices for resilient hybrid modular buildings.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT190100801

    Funder
    Australian Research Council
    Funding Amount
    $821,000.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP210103631

    Funder
    Australian Research Council
    Funding Amount
    $355,331.00
    Summary
    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.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP210100271

    Funder
    Australian Research Council
    Funding Amount
    $290,831.00
    Summary
    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.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT200100183

    Funder
    Australian Research Council
    Funding Amount
    $803,684.00
    Summary
    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.
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