AI-Enabled Design Diagnostics and Optimisation System for Mould Prevention (AI-DOMP)

The aftermath of the tragic death of Awaab Ishak because of prolonged exposure to mould in Rochdale Boroughwide Housing flats in 2020 increases the level of awareness of the health risks posed by mould. At the time, health practitioners were advocating that the requirements to address mould and dampness in housing should be given the same legal standing as gas safety checks (Brown, 2022). Till today, no specific regulation has emerged even though, there are several adversaries highlighting the roles of tenants and landlords in curbing this menace. Michael Gove, the levelling up, housing and communities secretary, said Awaab's death was "an unacceptable tragedy" and that the incident should be a defining moment for the housing sector in increasing knowledge and deepening understanding surrounding the issue of dampness and mould.

To this effect, research on how to develop Artificial intelligence (AI)-powered simulation system that is capable of analysing and optimising different building designs to predict potential mould growth is needed to establish the best design to avoid mould growth. As such, this project proposed a feasibility study for the development of an AI-enabled design diagnostics and optimisation system for mould prevention (AI-DOMP). The study will look at the possibility of developing an AI-enabled system that can analyse existing and new building designs to identify areas prone to mould growth and optimise such designs to mitigate the growth of mould in the areas and the whole building. The system will support building design for mould prevention using analysis of moisture-prone areas within the design to make preventive decisions on damp and mould growth. The proposed AI-enabled system will have two (2) key functionalities as follows:

1) **AI-enabled diagnostic platform for design analytics:** this platform will allow architects, designers, and maintenance officers to upload architectural designs with material components onto the system. The system will analyse the design and identify any area that is prone to dampness and mould growth. The identified mould or dampness-prone area will be classified into three main categories (i.e., Severe, Moderate and Mild) using air flow, humidity, and thermal performance level.

2) **AI-enabled design optimisation and decision support platform:** this will perform design optimisation to achieve a performance level that will prevent mould growth in the building. This will then form part of the design decision-making process for new builds and/or refurbishment of existing buildings.