Researchers from the School of Civil and Environmental Engineering have been successful in receiving funding for 5 ARC Discovery Projects (DP) for 2023.
The ARC DP scheme reflects the Australian Government’s commitment to excellence in research by supporting grant funding to support research projects that may be undertaken by individual researchers or research teams.
This is an amazing result for the school, and we congratulate the researchers and their collaborators on their success with receiving $2.2 million funding for their research projects. This continues recent successes in being awarded ARC funding for the school’s research and demonstrates the school’s reputation as being one of Australia’s leading research-intensive schools in Civil and Environmental Engineering.
Further details regarding the funded 2023 DP can be found below:
Project: Assessment of Dynamic Pile Driving Using Machine Learning
Investigators: Professor Hadi Khabbaz; Associate Professor Behzad Fatahi; Dr Di Wu; Dr Xue Zhang
Project Outline: This project aims at developing new technology to determine ground properties and foundation capacity in real-time during pile installation by adopting rigorous numerical simulation, laboratory experiments and artificial intelligence-based computational model. Although impact driving is used commonly to install piles on site, there is no technology currently available to interpret collected data accurately and in real-time to provide live feedback and optimise construction processes. This research will provide new machine learning model to assess the ground and foundation characteristics during construction and will increase certainty in infrastructure investment in Australia particularly for costly transport assets and infrastructure.
Project Funding: $412,353.00
Project: Response of Vertical Drains in Soft Subgrade under Cyclic Rail Loading
Investigators: Professor Buddhima Indraratna; Professor Cholachat Rujikiatkamjorn; Dr Richard Kelly; Professor Jian Chu.
Project Outline: Soft formations (subgrade) can become unstable when subjected to heavy and repeated (cyclic) train loading.
This project aims to investigate the cause and mechanisms of undrained instability of soft subgrade soil beneath rail embankments, and to assess the effectiveness of prefabricated vertical drains (PVDs) in stabilising such soils. The role of PVDs to enhance track performance will be quantified via rigorous mathematical techniques complementing a computer-based numerical model, which can be validated by laboratory and field data. It will deliver tangible outcomes for accurately predicting the long-term settlements in soft foundations over prolonged train loading while extending the life span of modern railroad infrastructure.
Project Funding: $569,000.00
Project: Real-time bridge performance evaluation based on crowdsourcing and learning
Investigators: Associate Professor Xinqun Zhu; Professor Jianchun Li; Associate Professor Yang Wang
Project Outline: This project aims to develop a novel strategy utilizing the real-time measurements from moving vehicles and bridges for evaluating the safety and operational performance of bridges based on transfer learning and vehicle-bridge interaction model. This is the first essential study on integrating the bridge-moving load models with transfer learning to extract common knowledge from simulation experiments to support the assessment of damaged status in practice. The project will provide an engineer-friendly low-cost monitoring system for its deployment, management and maintenance of existing transport infrastructure. The innovative techniques developed enable the safe operation and reliable evaluation and maintenance of transport infrastructure.
Project Funding: $360,000.00
Project: Modernise geotechnical investigation and analysis with machine learning
Investigators: Professor Daichao Sheng; Dr Xuzhen He; Professor Biswajeet Pradhan
Project Outline: The project aims to address the ineffectiveness associated with risk analysis of geotechnical systems by reducing variabilities and by rigorously quantifying such variabilities. It is expected to generate new knowledge in machine-learning-aided risk analysis and in virtual modelling of multiphase-multiphysics-multiscale problems involving random variables. Expected outcomes are datasets and computer tools that are equipped with new functionalities including parameter optimisation, uncertainty quantification, machine-learning based surrogate models and risk analysis. These tools will help to bridge the increasing gap between academic research and engineering practice, transform geo-risk analysis and optimise complex construction processes.
Project Funding: $450,000.00
Project: A novel ion-selective membrane for efficient lithium recovery
Investigators: Professor Ho Kyong Shon; Dr Gayathri Naidu; Dr Sherub Phuntsho; Dr Tao He; Professor Enrico Drioli
Project Outline: This project aims to fabricate a novel membrane that display selective lithium recovery from brine in a renewable energy driven electrochemical membrane technology. The fabrication of lithium selective membranes embedded with nanomaterials and metal organic framework will create new knowledge on the dynamics of ion-size sieving and accelerating lithium transportation. This project will provide significant environmental and economic benefit by establishing a rapid and chemical free method to recover lithium affordably and orders of magnitude more efficiently than hard rock extraction. This project will bring significant commercial benefits to Australian mining industry, desalination and water treatment sectors.
Project Funding: $426,400.00