About the Project
The modular (or prefabricated) construction is attracting a fresh wave of interest after realizing its advantages including sustainability benefits. Most of the construction phase is performed in the factory environment while leaving only the assembly phase to remain on-site. Modular construction has been a greater source to meet the urgent healthcare infrastructure requirement arisen during the Covid-19 pandemic. The UK Green Building Council statement demonstrate that around 400 million tonnes of materials are used by the UK construction industry each year. The statistics show the need of new eco-friendly and material saving building techniques to reduce the carbon footprint of the UK’s construction sector and to ultimately improve sustainability. Modular construction could potentially be a key driver in carbon footprint reduction in the UK construction sector as most of the works are performed in a controlled environment. Therefore, a detailed investigation of the modular building is necessary.
The overall aim of this research is to develop a novel lightweight and sustainable modular building system with enhanced structural performance through optimisation and emerging machine learning algorithms, experimental and finite element analysis. This project will enable the development of a new design methodology for modular buildings that could ultimately improve the structural performance, sustainability and minimises the use of materials and carbon footprint of the UK’s construction sector.
The specific tasks of this study are to:
1. Perform a detailed literature survey on modular buildings.
2. Optimisation of joists and studs’ dimensions for the minimal amount of material requirement.
3. Conduct full-scale structural tests for optimised joist and stud sections.
4. Develop Finite element models of optimised joist and studs to simulate their structural behaviours and capacities.
5. Validate the finite element model and perform parametric studies.
6. Assess the suitability of the current design standards for the structural capacities.
7. Investigate the sustainability performance of the system through its entire life cycle and end to end process.
8. Investigate the carbon footprint associated with the system and the process.
9. Use the machine learning algorithm to develop a design methodology combining the results of weight optimisation and sustainability investigations.
The Principal Supervisor for this project is Dr. Keerthan Poologanathan.
Eligibility and How to Apply:
Please note eligibility requirement:
· Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
· Appropriate IELTS score, if required.
· Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.
For further details of how to apply, entry requirements and the application form, see
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDFC21/EE/MCE/POOLOGANATHANKeerthan) will not be considered.
Deadline for applications: 29 April 2021
Start Date: 1 October 2021
Northumbria University is an equal opportunities provider and in welcoming applications for studentships from all sectors of the community we strongly encourage applications from women and under-represented groups.
* Please note that in order to be classed as Home Student, candidates must meet the following criteria:
- be a UK National (meeting residency requirements), or
- have settled status, or
- have pre-settled status (meeting residency requirements), or
- have indefinite leave to remain or enter.
2. Gatheeshgar, P., Poologanathan, K., Gunalan, S., Shyha, I., Sherlock, P., Rajanayagam, H. and Nagaratnam, B., (2021). Development of Affordable Steel-Framed Modular Buildings for Emergency Situations (Covid-19). Structures,31, pp.862-875
3. Gatheeshgar, P., Poologanathan, K., Gunalan, S., Shyha, I., Tsavdaridis, K. and Corradi, M., (2020). Optimal design of cold-formed steel lipped channel beams: Combined bending, shear, and web crippling. Structures, 28, pp.825-836.
4. Gatheeshgar, P., Poologanathan, K., Gunalan, S., Nagaratnam, B., Tsavdaridis, K. and Ye, J., (2020). Structural behaviour of optimized cold‐formed steel beams. Steel Construction, 13(4), pp.294-304.
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