AI-Driven Approaches for Enhanced Efficiency at Loughborough University

This project will develop automated methods to promote efficiency in the delivery and maintenance of assets using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The project will create an AI framework capable of managing large-scale asset inventories, predicting maintenance needs, and optimizing resource allocation. The system will employ ML algorithms to analyse historical data and predict future asset performance, identifying potential issues before they occur.

A pivotal feature of this initiative is the integration of Life Cycle Assessment (LCA) methodologies within the AI framework. This will enable a thorough assessment of the environmental impacts of assets throughout their lifecycle, including carbon footprint and resource usage. In addition, the project will incorporate building energy assessment tools to optimize the energy efficiency of assets. This aspect will focus on reducing energy consumption and operational costs, thus augmenting the sustainability aspect of asset management. The framework integrates with existing management systems to provide a comprehensive, real-time view of all assets and facilitate proactive maintenance strategies.

The project will also explore using natural language processing to automate the processing of maintenance logs and reports and increase efficiency in data management. By implementing this AI-driven asset management system, the research aims to significantly reduce downtime, extend asset lifespan, and lower maintenance costs. This research will considerably improve the efficiency and sustainability of asset management practices.

This project is one of five PhDs in Digital Transformation. The successful candidate will be part of a growing community of doctoral and post-doctoral researchers and academics who are extending the boundaries of knowledge and delivering transformative solutions to real-world problems. The other projects in Digital Transformation are:

• Automated productivity and performance monitoring for construction processes
• Automating the circularity potential assessment in building retrofitting
• Multi-actor transformation of production in construction: the case of novel concrete
• The sociotechnical interface with data in construction: aesthetics, emotion, and social discourse

Additional Funding Information

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Fees are reviewed annually and are likely to increase to take into account inflationary pressures.

The School of Architecture, Building and Civil Engineering’s studentship competition offers the chance for UK and International applicants who are interested in undertaking a PhD to have their full-time studies paid for.

The studentship is for 3 years and provides a tax-free stipend of £18,622 per annum (2023/24 rate) for the duration of the studentship plus university tuition fees.

Studentships will be awarded on a competitive basis to applicants who have applied to advertised projects starting with the reference ‘ABCE24’.