AI-driven and real-time command and control centre for site equipment in infrastructure projects

现场设备和设备（P&E），特别是重型土方设备，如挖掘机，推土机和卡车，是建筑项目的主要成本要素，从商业项目的10%到主要基础设施项目的50%，如高速公路，铁路线和能源项目。P&E是一种关键资源，通常会导致项目延迟，也是造成现场/场外拥堵和空气污染的主要原因（例如，它们占伦敦氮氧化物排放量的7%）。HS 2中的财团先前的研究表明，利用率低至30%;工作包之间的设备要求交叉，造成三到五倍的设备重复/冗余，以及现场拥挤导致的H&S风险和不必要的超支。长期以来，现场P&E一直是一个主要的盲点。在未来10年内，英国计划投资6000亿英镑的公共和私人基础设施。（TIP，2017），这是一个解决这一生产力问题并开发国际领先的英国解决方案的重要机会。在成功进行可行性研究后，我们测试了从现场P&E收集的实时数据，并使用机器学习来估计现场设备的生产力，该项目旨在将我们的解决方案推进到工业研究阶段，为基础设施项目的现场设备开发和测试第一个人工智能驱动的实时指挥和控制中心。该项目将通过开发新颖的“数字信息管理，工具，系统和标准”（即通过我们的指挥和控制仪表板支持人工智能）和“分析，基准和指标”（即通过生成建筑业基准数据）。

Site plant and equipment (P&E), particularly heavy earthmoving equipment such as excavators, bulldozers and trucks represent a major cost element in construction projects ranging from 10% in a commercial project up to 50% in major infrastructure projects such as highways, rail lines and energy projects. P&E are a critical resource that is often involved in project delays, and a major contributor to on/offsite congestion and air pollution (for example, they contribute up to 7% of London's NOx emissions).Previous research by the consortium within HS2 showed that utilisation rates are as low as 30%; crossover of equipment requirements between work packages causing three to five times equipment duplication/redundancy, and site congestion resulting in H&S risks and unnecessary overspend.Site P&E has been a major blind spot for a long time. With £600 billion of public and private infrastructure investment planned over the next 10 years (TIP, 2017), there is a significant opportunity to address this productivity issue and develop an internationally leading UK-based solution.Following the successful feasibility study where we have tested the collection of live data from site P&E and used machine learning to estimate productivity of site equipment, this project aims to advance our solution into the industrial research stage by developing and testing the first of its kind AI-driven and real-time command and control centre for site equipment in infrastructure projects.The project will contribute to the Transforming Construction ISCF Programme through the development of novel "digital information management, tools, systems and standards" (that is through our command and control dashboard supported with AI) and "analytics, benchmarking and metrics" (that is through the generation of construction earthwork benchmark data).