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

Awaab Ishak于二零二零年因长时间接触罗奇代尔区房屋单位的霉菌而不幸死亡，这增加了人们对霉菌造成的健康风险的认识。当时，卫生从业者主张，解决住房中霉菌和潮湿的要求应与气体安全检查具有相同的法律的地位（Brown，2022）。直到今天，还没有具体的规定出现，尽管有几个对手强调了租户和房东在遏制这种威胁方面的作用。平整，住房和社区部长迈克尔戈夫说，Awaab的死亡是“一个不可接受的悲剧”，这一事件应该是一个决定性的时刻，住房部门在增加知识和加深对潮湿和霉菌问题的理解。研究如何开发人工智能（AI）-我们需要一个有动力的模拟系统，能够分析和优化不同的建筑物设计，以预测潜在的霉菌生长，从而建立避免霉菌生长的最佳设计。因此，该项目提出了一项可行性研究，以开发一个基于AI的设计诊断和优化系统，用于模具预防（AI-DOMP）。该研究将研究开发一个支持人工智能的系统的可能性，该系统可以分析现有和新的建筑设计，以确定易于霉菌生长的区域，并优化这些设计，以减轻这些区域和整个建筑物中霉菌的生长。该系统将支持建筑物的防霉设计，通过分析设计中易潮湿的区域，对潮湿和霉菌生长做出预防性决定。拟议的人工智能支持的系统将具有以下两（2）个关键功能：1）** 用于设计分析的人工智能支持的诊断平台：** 该平台将允许建筑师，设计师和维护人员将建筑设计与材料组件上传到系统上。该系统将分析设计并识别任何容易潮湿和霉菌生长的区域。已确定的霉菌或潮湿易发区将分为三个主要类别（即，2）** 支持人工智能的设计优化和决策支持平台：** 这将执行设计优化，以达到防止建筑物中霉菌生长的性能水平。这将成为新建和/或翻新现有建筑物的设计决策过程的一部分。

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.