Civil engineering internships

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: Implementing Artificial Intelligence (AI) techniques in the enhancement of steel moment frame structures signifies a groundbreaking shift in how these essential engineering systems are designed, analysed, and optimized. This review covers a broad array of AI strategies, such as machine learning algorithms, evolutionary algorithms, neural networks, and advanced optimization methods, which are utilized to tackle various challenges within the sector.

The consolidation of these research findings underscores the interdisciplinary approach of AI in structural engineering, highlighting the integration of domain expertise with sophisticated computational methods. This comprehensive synthesis is a crucial resource for researchers, practitioners, and policymakers looking to grasp the cutting-edge developments in AI-enabled optimization of steel moment frame structures.

References: Mohsen Soori, Fooad Karimi Ghaleh Jough. Artificial Intelligent in Optimization of Steel Moment Frame Structures: A Review. International Journal of Structural and Construction Engineering, 2024.

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: Artificial intelligence encompasses a range of techniques and fields, such as vision, perception, speech and dialogue, decision-making, planning, problem-solving, robotics, and other areas conducive to autonomous learning. This study focuses on exploring the potential of AI algorithms to enhance safety throughout different phases of the construction process. The research reviewed the scientific literature on applying artificial intelligence in construction and optimising these processes.

References: https://www.mdpi.com/1424-8220/23/21/8740

Requirement to be on campus: No

Supervisor: Dr Emily Moylan

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: Public transit operators sometimes joke that the best way to get the buses to run on time is to never open the doors. Dwell time at stops is an important source of delay and unreliability in bus performance. Although the stochastic nature of boarding and alighting passengers is unavoidable, some aspects contributing to delay, such as the design of the vehicles and the design of the stops, are within the control of the operator. 

This project will make use of transit big data, including the GTFS-realtime API and transit smartcard data, to explore bus dwell times. The candidate should be comfortable working with or learning Python, and there is potential to focus on data visualisation, statistical or machine learning predictive models or GIS.

Requirement to be on campus: No

Supervisor: Dr Emily Moylan

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 friend told me that he stopped taking the M5 to work because there was a crash once, and he was caught in congestion for over an hour. It made me wonder what role extreme events (rare but drastically bad network performance) play in our travel choices.

The path that travellers choose through the network, usually referred to as route choice, is based on a comparison of time, distance and costs across the available alternatives. Previously, other aspects of the travel time distribution have been tested in the utility of the routes, namely travel time reliability and rare event reliability. These studies mostly used stated preference data where respondents are asked to choose between hypothetical alternatives. Revealed preference data is more realistic, but is challenging because the researcher usually doesn’t know what alternatives were available and known to be available to the respondent. 

This project explores the opportunities and limitations of revealed preference data for exploring the role of rare events in route choice modelling. The candidate should expect to work hands on with data and learn about discrete choice modelling using the Biogeme library in Python.

Requirement to be on campus: No

Supervisors: Dr Jiaying Li, Dr Haihui Joy Jiang (School of Chemistry)

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

Project Description: Effective removal of emerging contaminants (ECs) like perfluoroalkyl substances (PFAS) and pesticides in water is challenging due to their stable chemical structures and high operational expense of typical treatment technologies like membrane filtration and advanced oxidation. Plasma electrochemical technique shows a promising potential to destruct complex chemicals by triggering radical and electrochemical reactions across gas-liquid interfaces at room temperature.

In this project, we will conduct destruction experiments based on the laboratory-scale plasma reactors to evaluate the removal efficiencies of several ECs in water matrices. The project will be conducted in the laboratory, and chemical experiments and analysis will be 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 the government’s health advice.

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: Enzyme-induced carbonate precipitation (EICP) and microbially-induced carbonate precipitation (MICP) are two emerging bio-inspired techniques that can be used for soil improvement (e.g. to provide foundation support for buildings or to minimise dust on construction sites). Through life cycle assessment studies, EICP and MICP have been shown to produce low carbon emissions compared to existing, business-as-usual soil improvement techniques. However, both EICP and MICP produce ammonium chloride as a by-product which contributes to salinity and eutrophication impacts on the surrounding environment.

Through this project, you will identify and evaluate current and emerging techniques that can be used to remove ammonium chloride from EICP- and MICP-treated soil. You will also gain an understanding of life cycle assessment methods that can be used to evaluate the environmental impacts of ammonium chloride removal techniques. This work will be completed through a desktop literature review.

Requirement to be on campus: No

Supervisor: Dr Andres Fielbaum

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 Downs-Thomson paradox suggests that investments in car infrastructure worsen overall traffic conditions in the long term. This occurs due to induced shifts from public transport to private cars, leading to increased congestion and a decline in public transport service quality.

This paradox relies on the assumption of an equilibrium between the two modes, where both modes present equal total costs. However, this equilibrium is not always achieved, as other outcomes are possible—corner cases where one mode dominates the other for specific origin-destination pairs.

In this project, we will mode choice data from various cities in Australia and overseas to:

• Identify in which origin-destination pairs an equilibrium exists and where one mode dominates with the other, serving only captive users.
• Analyse the influence of distance and socio-economic factors, such as wealth, on these patterns.

Requirement to be on campus: No

Supervisor: Sabita Adhikari

Eligibility: WAM > 75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application. A basic understanding of humanitarian engineering and the context of water sanitation and hygiene in lower- and middle-income countries is preferred but not required.

Project Description: Access to safe water sanitation and hygiene (WASH) services in schools is essential for students' health, well-being, and right to basic education. Despite global and national efforts, many lower- and middle-income countries still have limited access to basic WASH services in schools. Thus, assessing the status of these services and identifying factors affecting them is crucial to informing effective WASH interventions in schools. 

This project aims to investigate the availability, functionality and equity of WASH services in schools using National Water Sanitation and Hygiene (NWASH) data from Nepal. Students will gain hands-on experience working with field data, data processing, visualisation and statistical analysis using Excel, R software and/or Geographical Information System (GIS) tools. At the end of the project, students may be required to produce a report or presentation summarising the findings. 

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

Supervisor: Associate Professor 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: This project explores the development of agentic large language models (LLMs) for civil engineering applications. Students will conduct a literature review, identify key challenges and opportunities, and develop a proof of concept based on their areas of interest.

The project is intentionally open-ended to encourage student-driven exploration and innovation. Ideal candidates will have a strong interest in emerging technologies and a willingness to engage in creative problem-solving.

Requirement to be on campus: No

Supervisor: Professor David Levinson, Dr Jennifer Kent (Architecture, Design, and Planning)

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: Hooning, including burnouts, street racing, and reckless driving, poses significant challenges to urban safety, road infrastructure, and environmental sustainability. This project will investigate the impacts of hooning and burnouts on air quality, noise pollution, road safety, and traffic flow. Using a mix of literature review, data analysis, and potential field observations, the research will aim to provide insights into the scale of these issues and possible mitigation strategies.

Research Objectives:

1. Environmental Impact: Assess the effects of burnouts and tyre friction on air quality, particulate emissions, and noise pollution.
2. Safety Considerations: Examine the risks posed by hooning-related incidents to pedestrians, cyclists, and other road users.
3. Traffic and Infrastructure Implications: Evaluate the disruption caused by hooning events to traffic flow and road conditions.
4. Policy and Enforcement: Explore existing regulations and enforcement strategies aimed at curbing hooning behaviour.

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 3rd – 4th year undergraduate students or postgraduate students.

Project Description: Emerging contaminants (ECs) like perfluoroalkyl substances (PFAS) and pesticides pose an increasing threat to human and environmental health. Given their widespread presence in the environment, there is an urgent need for rapid, practical, and cost-effective methods to detect ECs in water environments, including surface water, wastewater, and source water.

In this project, we will design and develop simple and inexpensive detection methods for PFAS using different materials such as nanoparticles or based on electrically read lateral flow assay. 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 in the laboratory, and chemical experiments and analysis will be 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 the government’s health advice