Development of machine learning models to predict properties of concrete.

Project description

This project focuses on developing machine learning models to predict key properties of concrete, such as compressive strength, based on its composition and curing conditions. By leveraging datasets and various ML algorithms, the study aims to replace time-consuming laboratory testing with accurate, data-driven predictions. The methodology includes data collection, preprocessing, model training, performance evaluation, and deployment of a user-friendly tool. Expected outcomes include an efficient ML model, insights into material behaviour, and a practical application for engineers to optimise concrete mix designs.

Supervisors research focus

Dr. Aliakbar Gholampour is a Senior Lecturer in Civil and Structural Engineering at the College of Science and Engineering, Flinders University. He earned his PhD in Structural Engineering from the University of Adelaide in 2019 and subsequently served as a Postdoctoral Research Fellow at the University of Melbourne until July 2020. Dr. Gholampour specialises in infrastructure materials and structural engineering, with a strong focus on sustainability and innovation. His expertise includes recycling waste materials, industrial by-products, and fibres to develop sustainable construction materials. He also works extensively on cementitious composites, nanomaterials, and fibre-reinforced concrete, including advanced modeling and design approaches. In the field of structural engineering, Dr. Gholampour's research addresses the behaviour, performance, and design of civil infrastructure, with a particular emphasis on resilience, sustainability, and effective management of deteriorating assets. His work extends to cutting-edge technologies such as 3D printable concrete and smart multifunctional construction materials.

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