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Research Topic : Capacity Building
Australian State/Territory : WA
Field of Research : Civil Engineering
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  • Active Funded Activity

    Linkage Projects - Grant ID: LP160100528

    Funder
    Australian Research Council
    Funding Amount
    $280,000.00
    Summary
    Bridge performance assessment through advanced sensing and modelling. Bridge performance assessment through advanced sensing and modelling. This project aims to create cyber infrastructure to manage and maintain civil infrastructure, specifically bridges. Current sensor data interpretation approaches are not good at assessing the performance of civil infrastructure or evaluating the reserve capacity; in particular, they do not adequately account for high levels of systematic modelling uncertaint .... Bridge performance assessment through advanced sensing and modelling. Bridge performance assessment through advanced sensing and modelling. This project aims to create cyber infrastructure to manage and maintain civil infrastructure, specifically bridges. Current sensor data interpretation approaches are not good at assessing the performance of civil infrastructure or evaluating the reserve capacity; in particular, they do not adequately account for high levels of systematic modelling uncertainties. This project intends to ease the current scientific data interpretation bottleneck. Expected outcomes are better infrastructure management and maintenance planning, fewer redundant interventions, modified infrastructure and improved future design.
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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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    Funded Activity

    Discovery Projects - Grant ID: DP160104731

    Funder
    Australian Research Council
    Funding Amount
    $370,000.00
    Summary
    Modelling and testing corroding reinforced concrete structures. The project aims to develop models and methods to enable the early detection of active steel corrosion. Most of Australia’s critical infrastructure is located on or near the coast in high saline conditions and is exposed to a high risk of reinforcing steel corrosion. Our ability to design and monitor such structures is crucial. The first part of the project aims to develop an innovative finite element model to improve the prediction .... Modelling and testing corroding reinforced concrete structures. The project aims to develop models and methods to enable the early detection of active steel corrosion. Most of Australia’s critical infrastructure is located on or near the coast in high saline conditions and is exposed to a high risk of reinforcing steel corrosion. Our ability to design and monitor such structures is crucial. The first part of the project aims to develop an innovative finite element model to improve the prediction of both active steel reinforcement corrosion and the time to concrete cracking in a chloride environment. It then plans to develop a non-destructive method, combining ultrasonic waves-based technology and acoustic emission, to detect active steel corrosion before any damage is visible on the structure.
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