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

**背景：**英国建筑公司直接受到Covid-19的影响，由于材料/劳动力通胀和Covid-19安全措施，他们的工作量和管理团队受到干扰，成本也在增加。该项目旨在优化英国中小企业建筑公司，通过将机器学习和人工智能应用于现实世界的问题，显著提高生产力并节省成本。**问题：**每年都有成千上万的房屋、扩建和小型工业项目委托客户，建筑师/设计师在不同的CAD系统上或手工准备图纸。然后，建筑公司通常会获得pdf格式的建筑图则，用于报价和施工，通常会有3-5家建筑商投标同一项目。结果是，数百万份标书被制作出来，每个项目只有一个成功的候选人，浪费了数百万英镑的成本和数小时的标书准备时间。因此，面对这种财务风险，匆忙做出估算，导致随后数字化管理成功投标的产出不佳。所需要的是一个软件工具，以帮助建筑商轻松地直接从建筑计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，包括所有相关的过程管理和文件，准备有效地建造建筑物。**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。应用ML/AI项目的愿景是加快创建所有实体（地板、墙壁、屋顶和开窗）的数字表示的过程，并从平面图中推断实体类型和几何形状。然后，机器学习/人工智能将所有实体数据传递给HBXL的现有软件，链接到规范、估算、项目规划和健康与安全管理系统。由此产生的输出可以在整个项目团队之间共享，并在HBXL现有的施工管理软件中使用，并很快发布基于云的施工云。**为什么资助这个创新项目？**该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部门交易中概述的双重目标，重点关注设计、建设和管理的数字和人工智能方法。这种及时的方法将使小型项目的整个施工过程自动数字化，节省每个项目数天的准备时间，并在实际施工过程中产生至少5-10%的生产率增长（ONS, 2018）。该项目有可能改变被数字技术排斥、时间紧迫的中小企业建筑商使用数字方法的方式，提供易于使用的工具，这将节省他们大量的时间，同时提高交付速度、质量和可持续性。

**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.