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

建筑、拆除和挖掘(CD&E)活动产生了1.378亿吨垃圾，占2018年英国产生的垃圾总量的62%(环境、食品和农村事务部，2022年)。废物对建筑业的影响是巨大的，每年的经济成本约为110亿GB，对利润的不利影响，以及对环境和健康的负面影响(绿色建筑委员会，2020年)。2011年《废物(英格兰和威尔士)条例》倡导在废物管理中采取分级办法--减少、再利用、回收、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管理废物的最好解决方案。_这涉及使用更少的材料，以及采用其他设计或建造方法，从而减少材料使用量和浪费水平_(BREAAM，Mat 06)。尽管材料效率是防止和减少建造业浪费的最好选择，但废物处理是普遍存在的。由于缺乏早期阶段的废物设计，在施工阶段产生了大量可避免的废物(绿色建筑委员会，2020)。建筑企业使用的人工方法耗时长，容易出错，而且不是最优的。设计废物是一个复杂的优化问题，需要选择设计的最佳维度协调和产生最少废物的材料标准化。人工智能领域的进步和大数据分析为优化设计以减少废物产生提供了机会，这是拟议项目寻求实施的。该项目旨在创建一个人工智能系统(AI-DOWEP)，作为基于BIM的解决方案，包括以下两个关键要素：a.支持人工智能的废品效率设计诊断平台：该平台对建筑设计进行诊断，以评估其废品效率指数，并自动突出显示潜在的废品最小化区域。这将由对现有和容易获得的材料标准、规格、形状和其他人体测量数据的实时数据分析推动。支持人工智能的设计优化平台：这将利用人工智能进化算法来优化建筑设计，以符合预期的功能和设计人体测量的垃圾效率。它将解决“什么是最优设计、尺寸协调和材料标准化，才能产生最少的废物”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'.