Using ML/AI to fast-track SME construction digitisation adoption to improve UK building productivity

使用机器学习/人工智能快速跟踪中小企业建筑数字化的采用，以提高英国的建筑生产力

• 批准号: 76654
• 金额: $ 22.12万
• 依托单位: HBXL GROUP LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=76654
• 关键词: Using; ML; AI; fast; track

**Backdrop:**UK building firms have been directly impacted by Covid-19, with their workloads and management teams disrupted along with increasing costs due to materials/labour inflation and Covid-Secure measures.The project aims to optimise UK SME building firms, making significant productivity and cost savings using Machine Learning and AI applied to a real-world problem.**Problem:**Every year hundreds of thousands of houses, extensions and small industrial projects are commissioned by clients, with Architects/Designers preparing drawings on varying CAD systems or by hand.Building firms are then typically provided with building plans as PDFs for quotation and construction, typically with 3-5 builders tendering for the same project. The result is millions of tenders being produced with only one successful candidate per project, wasting millions in £cost and hours of tender preparation time.Consequently, in the face of this financial risk, estimates are produced in haste, resulting in poor outputs to subsequently digitally manage successful tenders.What is needed is a software tool to assist builders easily ascertain direct from the building plan all labour and materials, all associated costs and prepare a detailed project plan, reflecting the content of the work and the builders own resources, including all associated process management and documentation, ready to efficiently construct the building.**Solution:**Machine Learning and AI (ML/AI), combined with HBXL's existing software can solve this problem with funding.The applied ML/AI project vision is to speed up the process of creating digital representations of all entities (floor, walls, roof and fenestration), and infer the entities type and geometry from the plan.The ML/AI will then pass all entity data to HBXL's existing software, linking to specification, estimating, project planning and health & safety management systems.The resulting output can then be shared amongst and utilised by the entire project team in HBXL's existing construction management software and soon to be released cloud-based Construction Cloud.**Why fund this innovative project?**This innovative project addresses Government's twin goals outlined in the Industrial Strategy Challenge Fund Construction Sector and AI Sector Deals, focusing on Digital and Artificial Intelligence approaches to design, construction and management.This timely approach will automatically digitise the entire construction process for smaller projects, saving days of preparation time on each project and produce at least 5-10% in productivity growth during actual construction (ONS, 2018).The project has the potential to transform the way digitally excluded less tech-savvy, time pressured SME builders engage with a digital approach, delivering easy-to-use tools which will save them large amounts of time whilst simultaneously improving speed of delivery, quality and sustainability.

**背景：**英国建筑公司受到 Covid-19 的直接影响，由于材料/劳动力通胀和 Covid-Secure 措施导致成本增加，他们的工作量和管理团队受到干扰。该项目旨在优化英国中小企业建筑公司，利用机器学习和人工智能解决现实问题，显着提高生产力并节省成本。**问题：**每年都有数十万套房屋、扩建和小型工业项目受客户委托，由建筑师/设计师准备图纸在变化的CAD 系统或手工。然后，通常向建筑公司提供 PDF 格式的建筑平面图，用于报价和施工，通常有 3-5 个建筑商对同一项目进行投标，结果是产生数百万份标书，每个项目只有一名成功的候选人。浪费了数百万英镑的成本和数小时的投标准备时间。因此，面对这种财务风险，估算是匆忙进行的，导致随后以数字方式管理成功投标的产出不佳。我们需要的是一种能够轻松帮助建筑商的软件工具直接从建筑规划中确定所有劳动力和材料、所有相关成本，并准备详细的项目计划，反映工作内容和建筑商自己的资源，包括所有相关的流程管理和文件，为高效建造建筑做好准备。**解决方案：**机器学习和人工智能（ML/AI）结合HBXL现有的软件可以通过资金解决这个问题。所应用的ML/AI项目愿景是加快创建所有实体（地板、墙壁）数字表示的过程、屋顶和开窗），并从计划中推断出实体类型和几何形状。然后，ML/AI 将所有实体数据传递到 HBXL 的现有软件，链接到规格、估算、项目规划以及健康和安全管理系统。然后，生成的输出可以在以下人员之间共享：整个项目团队在 HBXL 现有的施工管理软件中使用，并且即将发布基于云的施工云。**为什么资助这个创新项目？**这个创新项目解决了政府在工业战略挑战基金建设部门和人工智能。行业交易，专注于设计、施工和管理的数字和人工智能方法。这种及时的方法将自动数字化小型项目的整个施工过程，节省每个项目数天的准备时间，并产生至少 5-10% 的生产力增长在实际施工期间（ONS，2018）。该项目有可能改变数字化排斥技术不太精通、时间紧迫的中小企业建筑商采用数字化方法的方式，提供易于使用的工具，这将为他们节省大量时间同时提高交付速度、质量和可持续性。