Collaboration to bring machine-learning and AI into social housing maintenance

England is home to 1,600 registered social housing providers managing over 4.4 million properties that require statutory inspections to check gas, asbestos and water hygiene, plus general upkeep.

Currently, there isn’t a scheduling system that offers integration between maintenance and safety contractors, resulting in additional site visits, increased travel costs and re-work.

Aston University computer scientists will use machine-learning and AI to create a maintenance prioritisation system that will centralise job requests and automatically allocate them to the relevant contractors.

The collaboration is being made through a Knowledge Transfer Partnership (KTP), a partnership between a business, an academic partner and a suitable researcher, known as a KTP associate.

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The latest work builds on Aston’s involvement in developing a system for TL that directs its field staff to jobs. According to Aston, the project team will improve the system to enable it to interact with client and contractor systems, by combining an input data processing unit, enhanced optimisation algorithms, customer enhancements and third-party add-ons into a single dynamic system.

The Aston University team will be led by Aniko Ekárt, Professor of Artificial Intelligence.

“It is a privilege to be involved in the creation of this system, which will select the best contractor for each job based on their skill set, availability and location and be reactive to changing priorities of jobs," she said in a statement.
TL provides asbestos consultancy, project management and training to businesses, local authorities, social housing and education facilities, using a fleet of mobile engineers across the UK.

John Richards, managing director at Thames Laboratories, said: “This partnership will allow us to adopt the latest research and expertise from a world-leading academic institute to develop an original solution to improving the efficiency of social housing repairs, maintenance and improvements to better meet the needs of social housing residents.” 

Professor Ekart will be joined by Dr Alina Patelli as academic supervisor. Dr Patelli brings experience of software development in the commercial sector as well as expertise in applying optimisation techniques with focus on urban systems.

She said: “This is a great opportunity to enhance state-of-the-art optimisation and machine learning in order to fit the needs of the commercial sector and deliver meaningful impact to Thames Laboratories.”