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.