AI-enabled Design Optimization for Waste Efficiency (AI-DoWEP)

建筑、拆除和挖掘（CD &E）活动产生了1.378亿吨废物，占2018年英国废物总量的62%（环境食品和农村事务部，2022年）。废弃物对建筑行业的影响巨大，每年的经济成本约为110亿英镑，对利润产生不利影响，并对环境和健康产生负面影响（绿色建筑委员会，2020年）。2011年《废物（英格兰和威尔士）条例》倡导在废物管理方面采取分级办法-减少、再利用、再循环、回收和处置-减少废物是最可取的选择。因此，材料效率是建筑项目有效废物管理的最佳解决方案。_这涉及使用更少的材料，并采用替代设计或施工方法，从而减少材料使用和降低浪费水平_（BREAAM，Mat 06）。尽管材料效率是防止和减少建筑行业浪费的最佳选择，但废物处理仍很普遍。由于缺乏在早期阶段设计废弃物，在建筑阶段产生大量可避免的废弃物（绿色建筑委员会，2020年）。建筑企业采用手工方法，耗时，容易出错，而且不是最佳方法。设计出废物是一个复杂的优化问题，选择最佳的设计尺寸协调和标准化的材料，将产生最少的浪费。人工智能领域的进步，大数据分析提供了优化设计的机会，以减少废物的产生，拟议的项目寻求实施。该项目旨在创建一个基于BIM的AI系统（AI-DOWEP），包括以下两个关键要素：a.基于人工智能的废物效率设计诊断平台：该平台诊断建筑设计，以估计其废物效率指数，并自动突出显示潜在的废物最小化区域。这将通过对现有和易于获取的材料标准、规格、形状和其他人体测量学的实时数据分析来推动。AI支持的设计优化平台：这将利用AI进化算法来优化建筑设计，以实现符合预期功能和设计人体测量学的废物效率。它将解决“什么是最佳的设计尺寸协调和材料标准化，将产生最少的浪费”的优化问题。

137.8 million tonnes of waste are generated from construction, demolition, and excavation (CD &E) activities and account for 62% of the total waste generated in the UK in 2018 (Department for Environment Food & Rural Affairs, 2022). The impact of waste on the construction industry is enormous with an economic cost of about £11 billion per annum, adverse effects on profit, and negative environmental and health impacts (Green Construction Board, 2020). The Waste (England and Wales) Regulations 2011 advocates for a hierarchical approach in waste management -- reduction, reuse, recycling, recovery, and disposal -- with waste reduction being the most preferable option. Consequently, material efficiency is the most preferable solution to effective waste management on construction projects. _This involves using fewer materials, and the adoption of alternative means of design or construction that results in lower material usage and lower wastage level_ (BREAAM, Mat 06).Despite material efficiency being the most preferable option for preventing and reducing waste in the construction industry, disposal of waste is prevalent. Lots of avoidable wastes are generated during the construction stage due to a lack of designing out waste at an early stage (Green Construction Board, 2020). Construction businesses employ manual approaches which are time-consuming, susceptible to errors and not optimal. Designing out waste is a complex optimization problem of selecting optimal dimensional coordination of design and standardization of materials that would generate the least waste.Advances in the field of Artificial intelligence, and big data analytics offer opportunities for optimizing design to reduce waste generation which the proposed project seeks to implement. The project aims to create an AI-enabled system (AI-DOWEP), as a BIM-based solution, consisting of two key elements as follows:a. AI-enabled Design Diagnostic Platform for Waste Efficiency: This diagnoses building designs to estimate its waste efficiency index, and automatically highlight areas for potential waste minimisation. This would be driven by real-time data analytics of available and readily accessible material standards, specifications, shapes and other anthropometrics.b. AI-enabled Design Optimisation Platform: This would leverage AI Evolutionary algorithms to optimise building designs for waste efficiency in line with intended functionalities and design anthropometrics. It would solve the optimization problem of 'what is the optimal design dimensional coordination and material standardization that would generate the least waste'.