Discovery Early Career Researcher Award - Grant ID: DE220100909
Funder
Australian Research Council
Funding Amount
$350,000.00
Summary
Innovative Soft-computing for Condition Assessment of Large Infrastructure. Health conditions of large infrastructure, such as bridges, have been difficult to determine due to their large scales, associated incomplete data and high uncertainties in measurement and system identification. This project will develop an innovative condition assessment method based on the advancements in structural dynamics analysis, multi-objective topology and soft-computing techniques, for reliably evaluating the h ....Innovative Soft-computing for Condition Assessment of Large Infrastructure. Health conditions of large infrastructure, such as bridges, have been difficult to determine due to their large scales, associated incomplete data and high uncertainties in measurement and system identification. This project will develop an innovative condition assessment method based on the advancements in structural dynamics analysis, multi-objective topology and soft-computing techniques, for reliably evaluating the health conditions of large infrastructure. The outcomes will enhance the current practices in infrastructure asset management to deliver timely retrofitting and extended life cycle. The development will provide benefits to Australia by enhancing operational efficiency and preventing catastrophic failure of infrastructure.Read moreRead less
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.Read moreRead less
Non-contact Integrity Assessment of Façade Panels of High-rise Buildings. Disintegration of the external façade (with tiles, plates, etc.) of high-rise buildings presents a great challenge and a threat to community. This project develops fundamental knowledge and algorithms that underpin the deployment of a new technique for fast and automated quantitative integrity assessment of façade units of high-rise buildings, integrating mechanisms of directional acoustic waves, vibro-acoustics of façade ....Non-contact Integrity Assessment of Façade Panels of High-rise Buildings. Disintegration of the external façade (with tiles, plates, etc.) of high-rise buildings presents a great challenge and a threat to community. This project develops fundamental knowledge and algorithms that underpin the deployment of a new technique for fast and automated quantitative integrity assessment of façade units of high-rise buildings, integrating mechanisms of directional acoustic waves, vibro-acoustics of façade tiles or panels, laser sensing technology, deep learning algorithms and drone technology. Outcomes of this project are critical for implementing the new technology for enhanced safety to community and the development of new procedures for driving down maintenance costs of the external façade of high-rise buildings.Read moreRead less
Functionally graded modelling of geopolymer and Portland cement concretes. This project aims to investigate why geopolymer concretes crack less than the Portland cement concretes. Carbon emissions from Portland cement is second only to fossil fuels. Geopolymer may emit less carbon dioxide than Portland cement concrete because it bleeds less and has higher creep. The project will test this hypothesis on geopolymer and Portland cement concretes, and emulate its findings in all types of concretes. ....Functionally graded modelling of geopolymer and Portland cement concretes. This project aims to investigate why geopolymer concretes crack less than the Portland cement concretes. Carbon emissions from Portland cement is second only to fossil fuels. Geopolymer may emit less carbon dioxide than Portland cement concrete because it bleeds less and has higher creep. The project will test this hypothesis on geopolymer and Portland cement concretes, and emulate its findings in all types of concretes. The project intends to create a specific market for geopolymer by developing a crack-free pavement and floors technology, while understanding cracking in concrete will reduce cracking issues.Read moreRead less
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.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