Rapid Continuous Improvement Platform through Generative AI: A feasibility study of Automation in Construction Cost Budgeting (RACIP-COST)

通过生成式人工智能快速持续改进平台：建筑成本预算自动化的可行性研究（RACIP-COST）

• 批准号: 10080485
• 金额: $ 5.72万
• 依托单位: MILAN CONSULTING LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10080485
• 关键词: Rapid; Continuous; Improvement; Platform; through

Construction micro-small and medium enterprises in the UK have been inundated with low productivity and a lack of growth. Only 27% of SMEs grew in 2022 (Satista, 2023). Additionally, construction SMEs in the UK experience labour shortages required for their growth. The Department for Business, Energy and Industrial Strategy (2023) and the Office for Artificial Intelligence (2023) both advocated for SME growth in the UK through AI. In line with these government policies, AI is needed to support growth through productivity enhancements in construction cost management continuously. Construction SMEs experience financial issues for cost, and overheads hinged on non-value-adding activities, such as excessive staff hours without automation. Generative AI is a machine learning tool capable of reducing staff hours required for cost management. Furthermore, a continuous improvement tool guided through automation, such as Generative AI in text-to-text outputs or graphics-to-text, will considerably reduce overheads in construction and engineering projects. The construction budgeting process leads to the production of bills of quantities (BOQ). This process necessitates directly and accurately quantifying construction materials and tasks required for a construction or engineering project. As part of this construction budgeting process, the individual rates are multiplied against each described quantity to produce detailed costs. The accuracy of BOQs has been critiqued as the leading cause of cost overrun and productivity issues through corrective measures (Love et al., 2018; Omotayo et al., 2022). These cost-budgeting tasks are tedious and produce considerable systematic errors. In the last decade, there has been an emergence of computer-aided design (CAD) measurement and budgeting software such as BlueBeam, RIB CostX, REVIT, and PlainSwift, to mention a few. The challenge with these software applications that are now related to the fifth dimension of Building Information Modelling (5D BIM) is the high cost of annual licensing and subscriptions, the technical knowledge required to produce BOQs and regular training costs and requirements.A Generative AI platform in the form of a software application will support construction SMEs in producing more accurate costs, reducing errors and producing an approach for continuous improvement. Therefore, through prototype software, this feasibility study aims to demonstrate how construction SMEs can create a path for growth through continuous improvement via cost budgeting. The RACIP-COST application is about continuous improvement through machine learning for a much larger application in other sectors, such as Performance measurement, procurement and control.

英国的建筑微型企业和中型企业被低生产率和缺乏增长所淹没。 2022年，只有27％的中小型企业增长（Satista，2023年）。此外，英国的建筑中小企业会经历其成长所需的劳动力短缺。商业，能源和工业战略部（2023年）和人工智能办公室（2023）均主张通过AI在英国的中小企业增长。与这些政府政策一致，需要AI来通过不断提高建筑成本管理的生产率来支持增长。 Construction Smes经历了财务问题的成本，并且悬在非价值辅助活动的间接费用，例如没有自动化的员工小时数。生成AI是一种机器学习工具，能够减少成本管理所需的员工小时。此外，通过自动化为指导的连续改进工具，例如文本到文本输出中的生成AI或图形到文本，将大大减少建筑和工程项目的开销。施工预算过程导致数量生产（BOQ）。此过程需要直接准确地量化建筑或工程项目所需的建筑材料和任务。作为此施工预算过程的一部分，将个体费率与每个所述数量的数量相乘，以产生详细的成本。 BOQ的准确性通过纠正措施批评为成本超支和生产力问题的主要原因（Love等，2018； Omotayo等，2022）。这些成本限制的任务是乏味的，并产生了相当大的系统错误。在过去的十年中，出现了计算机辅助设计（CAD）测量和预算软件，例如Bluebeam，Rib Costx，Revit和Plainswift。这些软件应用程序现在与建筑信息建模（5D BIM）相关的软件应用程序所面临的挑战是年度许可和订阅的高成本，生产BOQ和常规培训成本和要求的技术知识。软件应用程序形式的生成AI平台将支持构建中小企业在产生更准确的成本，降低错误的错误，降低错误的方法，并进行连续改进。因此，通过原型软件，这项可行性研究旨在证明建筑中小企业如何通过通过成本预算持续改进来创造增长的途径。 Racip-COST应用程序是通过机器学习的持续改进，以在其他领域（例如绩效测量，采购和控制）中进行更大的应用。