Civil engineering

Supervisor: Dr Aaron Opdyke

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

Asia and the Pacific is the region most affected by disaster displacement worldwide, with more than 225 million displacements occurring within country borders over the last decade. Climate change is set to heighten these impacts and complicate future displacement patterns.

This project aims to inventory vulnerability of housing to disasters and climate change impacts. It sets out to map the geographic coverage of existing vulnerability functions in three pilot countries – Philippines, Indonesia, and Fiji. National census data will be used to classify building stock characteristics and create nationwide coverage maps for existing housing vulnerability measures using Geographical Information Systems (GIS) tools.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisor: A/Prof Yixiang Gan

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.  

Project Description: 

The shear strength of soil is an essential engineering parameter used in geotechnical designs, e.g., earth dams, retaining walls, and slope stability assessments. Traditional methods to determine the shear strength include soil triaxial tests, which can be costly and time-consuming. In recent decades, machine learning methods have proven efficient in generating prediction models for material properties.

This research project aims to investigate and compare the performance of different machine learning models in predicting the shear strength of soils. The student needs to collect and clean a large amount of experimental dataset and determine the input parameters, such as, density, liquid limit, plastic limit, clay content, moisture content, and remold/in-situ conditions.

Various machine learning methods will be adopted to establish the correlation between input parameters and soil shear strength. A successful machine learning model can serve as a guideline to compare, verify, and predict testing results.  

Requirement to be on campus: No

Supervisor: Dr Jiaying Li

Eligibility: This project is open to applications from students with a background in sensing techniques and analytical chemistry, ideally 3^rd – 4^th year undergraduate students or postgraduate students.

Project Description:

Emerging contaminants (ECs) like perfluoroalkyl substances (PFAS) pose an increasing threat to human and environmental health. Given their widespread presence in the environment, there is an urgent need in Australia for rapid, practical, and cost-effective methods to detect ECs in water environments, including surface water, water supply sources, and wastewater. In this project, we will design and develop simple and inexpensive detection methods for PFAS using an electrically read lateral flow assay (e-LFA).

In this project, students will gain hands-on experience in designing and developing novel sensors for detecting ECs in water samples. The project will be conducted on-site, with laboratory experiments required. Students may be asked to produce a report or presentation summarizing their work at the end of the project.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisor: Dr Jiaying Li

Eligibility: This project is open to applications from students with a background in GIS and data science, ideally 3^rd – 4^th year undergraduate students or postgraduate students.

Project Description:

Pharmaceutical consumption level serves as an important indicator of public health and wellbeing. Wastewater testing can provide population-level estimates of pharmaceuticals within a catchment area. In this project, we will link wastewater data to population socioeconomics using Geographical Information System (GIS) and identify the drivers of changes in pharmaceutical use behaviours across different communities.

In this project, students will gain hands-on experience in data collection, data processing, GIS mapping, and multivariable analysis. The project will be conducted on-site, with desktop work using GIS software required. Students may be asked to produce a report or presentation summarizing their work at the end of the project.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisor: Dr Jiaying Li

Eligibility: This project is open to applications from students with a background in computer science, data science and epidemiology modelling, ideally 3^rd – 4^th year undergraduate students or postgraduate students.

Project Description:

International flight is a key vector for global disease spread. Testing wastewater of international flights arriving in Australia provides a novel approach to monitoring the importation of emerging pathogenic viruses from overseas. In this project, we will develop a computation model to design the best, cost-effective sampling approach for the timely detection of emerging viruses in international aircraft wastewater as a novel early warning system.

In this project, students will gain hands-on experience in model construction, programming, and hypothesis testing. The project will be conducted on-site, with desktop computational and programming works required. Students may be asked to produce a report or presentation summarizing their work at the end of the project.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisor: Dr Faham Tahmasebinia

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

Steel–glass composite frames are advantageous compared with traditional steel frames. The combined use of steel and glass diversifies the structural design. Compared with traditional steel structures with straight members, the irregular shapes of glass–steel structures allow designers to express their design concepts in more artistic ways, which improves the aesthetic value of the designed structure.  

In this research, the loading performance of the glass spindle torus in different cases will be investigated using two numerical modelling packages, Strand7 and ABAQUS. The research methodology will be exhibited high applicability on other case studies.  With these two powerful finite element modelling tools, the loading performance of other structures can also be captured by developing new structural models.

The most effective thickness and size of the stiffener to prevent local buckling will be explored. Finally, structural performance of the designed structure under serviceability limit state with vertical supports configured in the central opening will be comprehensively investigated.

Requirement to be on campus: No

Supervisor: Dr Faham Tahmasebinia

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

The energy performance prediction of buildings plays a significant role in the design phases. Theoretical analysis and statistical analysis are typically carried out to predict energy consumption. However, due to the complexity of the building characteristics, precise energy performance can hardly be predicted in the early design stage.

This study considers both building information modelling (BIM) and statistical approaches, including several regression models for the prediction purpose. This research also highlights a number of findings of energy modelling related to building energy performance simulation software, particularly Autodesk Green Building Studio.

In this research, the geometric models were created using Autodesk Revit. Based on the energy simulation conducted by Autodesk Green Building Studio (GBS), the energy properties of number of prototype and case study models will be determined. The GBS simulation will be carried out using DOE 2.2 engine. Some key parameters will be demonstrated used in BIM, including building type, location, building area, analysis year, floor-to-ceiling height, floor construction, wall construction, and ceiling construction. The Monte Carlo simulation method will be performed to predict precise energy consumption.

Requirement to be on campus: No

Supervisor: Dr Faham Tahmasebinia

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

A rock burst is an uncontrolled failure that releases a massive amount of kinetic energy, inducing excessive displacement of rock mass. Combined support to controlling rock dynamic failures is an essential part of the rock burst management. In the design of rock support, it is essential to consider not only the capacity of the individual elements but also their compatibility with each other and their interactions.

This research is aimed to develop a novel full-scale numerical procedure to evaluate the behaviour of individual support elements and their interactions under dynamic loads using Finite Element Commercial Package ABAQUS/Explicit. A mutual interaction relationship between two indicators, namely Cable/Rock Bolt Dissipated Energy and the Steel Mesh Dissipated Energy is also developed, and an interaction diagram will be provided. The proposed design concept can be used for the selection of an appropriate support system for coal burst management.

Requirement to be on campus: No

Supervisor: Dr Aisha Faruqi

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

Renewable energy technologies are increasingly being deployed to decarbonise electricity grids, with solar and wind being the fastest growing renewable energy sources. To manage the intermittent electricity supply provided by photovoltaics and wind turbines, energy storage systems such as batteries, pumped hydro, and hydrogen are needed. Understanding the life cycle carbon emissions of electricity generation from different renewable energy sources and energy storage systems is key to aid decision-making when aiming to meet net zero targets.

Through this research project, you will apply carbon foot printing methods to estimate the life cycle carbon emissions of using hydrogen for grid-scale energy storage. You will use Excel and/or Python to model each stage of the storage system life cycle from hydrogen production through to energy use. You will also evaluate the influence that modelling uncertainties have on the carbon footprint of hydrogen energy storage.

Requirement to be on campus: No

Supervisor: Dr David Levinson

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

This full project addresses the ‘Numbers-in-Safety’ question – how improvements in traffic and personal safety affect walk and bike demand systematically. The full project requires systematically investigating the causal chain between infrastructure, safety, perception of safety, and use of sustainable transport.

The specific research will measure aspects of the causal changing, measuring safety using camera and trajectory data (or collecting crash and crime data) and/or perception of safety from multiple sites as well pedestrian and bike demand at those sites, and, while controlling for other conditions, aim to infer the effect of safety on demand.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisor: A/Prof. Daniel Dias-da-Costa

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

A timely detection of defects in structural elements is crucial for ensuring the safety and longevity of infrastructure. The early identification of deficiencies can allow for an early intervention, thus reducing the likelihood of major failures. This project will implement and compare different crack segmentation techniques with the purpose of characterising surface damage in different materials.

A suitable dataset will also be created for the purpose of this research. The candidate will develop a strong knowledge about machine learning and artificial intelligence and be able to identify the most suitable models for industry deployment.

Requirement to be on campus: Yes *dependent on government’s health advice