Analysis of the evolution of cementitious materials

The chemical reactions within the binder component, and the changes in microstructure and phase composition during this process, are important factors that affect the performance of concrete. In order to adequately address the limitations of conventional models that struggle with extensive and intricate data, the integration of machine learning (ML) into concrete research emerges as a significant advancement. Apart from data processing, image processing stands out as a crucial functional subset of ML, featuring a notable category of algorithms known as Convolutional Neural Network (CNN).

This project aims to use CNN to analyse the microscopic evolution of cementitious materials at different ages. Outputs obtained through scanning electron microscopy (SEM) and backscattered electron (BSE) images will be used. These will be supported by well-established quantitative analysis methods including Quantitative X-ray Diffraction (QXRD), Thermogravimetric Analysis (TG), and Fourier-transform Infrared Spectroscopy (FTIR). Furthermore, X-Ray Computed Tomography (X-CT) will be employed to investigate the pore distribution within the samples in addition to the phase composition.

While consistently refining the CNN algorithm to enhance the precision of calculation outcomes, complementary machine learning models for data analysis, such as back propagation neural networks (BPNN), are essential for comprehending the influence of phase composition and porosity on the mechanical properties of hardened samples. Therefore, the project will also focus on cultivating a machine learning model with a dependable performance, leveraging a given database for training.

The successful candidate will join the Manchester CREATES (Concrete Materials, Resource Efficiency and Advanced Technology for Sustainability) team. For more details on research team activities, please visit www.manchestercreates.com

Eligibility

The minimum academic entry requirement for a PhD in the Faculty of Science and Engineering is an upper second-class honours degree (or international equivalent) in a discipline directly relevant to the PhD OR any upper-second class honours degree (or international equivalent) and a Master’s degree merit (or international equivalent) in a discipline directly relevant to the PhD.

Full entry requirements

Funding

At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply.

How to apply

To be considered for this project you’ll need to complete a formal application through our online application portal.

When applying, you’ll need to specify the full name of this project, the name of your supervisor, how you’re planning on funding your research, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

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