We aim to develop and launch a product called 'Aquila', a novel AI Digital Twin tool for construction sites. Deficiency in construction site productivity stems from inadequate control methods unable to provide bi-directional control loops (from project control office to site, and vice-versa), resulting in suboptimal work programmes, inefficient resource utilisation, logistical bottlenecks, and, consequently, project delays, escalated costs and increased emissions. For instance, heavy plants and equipment (P&E), accounting for over 40% of total project costs and often linked to issues of project delays, congestion and environmental emissions, have i) shockingly low P&E utilisation rates (as-low-as 30%), and ii) unnecessary duplication/redundancy (3-to-5 times). P&E are one of the biggest productivity blind spots within the construction sector, a substantial waste of resources given the UK's £650 billion infrastructure pipeline investment by 2025 and required P&E resources (~£3b billion/year), representing both a major industry need and market opportunity.

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).

"The project, by targeting the productivity gap caused by construction equipment fleet - which represents major cost elements in most construction projects- is entirely aligned with the ISCF, in particular with the main theme of improving performance through digitally enabled solutions. The proposed solution is scalable and adaptable across the industry (building, rail, transport, highways, and utilities). The aim of the project is to explore the feasibility of improving productivity on site by 15% or more by increasing plant and equipment utilisation throughout the construction phase. This will be done by monitoring equipment output via on board IoT sensors; identifying patterns in equipment usage data to enable optimal planning; linking equipment output to the 4D BIM model; and visualising the data through and intuitive dashboard that will provide critical analytical information to contractors and the supply chain out on site. Effective equipment fleet-management provides opportunities for productivity gains for client, contractors, subcontractors, plant hire companies and the public (pollution/noise). Research by the consortium partners with HS2 at London Bridge and Crossrail show: utilisation rates are as low as 30%; 5x equipment duplication; crossover of equipment requirements between work packages, and site congestion resulting in H&S risks. Despite this significant impact on productivity, environment and safety, equipment fleets are still a major blind spot within construction because of the lack of data and adequate digital ecosystems. Earlier work by the consortium in HS2 confirmed demand by the construction supply chain for systems for equipment fleet-management (i.e. estimation/selection, deployment, coordination, and visualisation) pending key limitations being resolved. The project seeks to establish our position as one of the first and leading tech platforms combining IoT, BIM and data analytics."

Buildings are becoming increasingly ‘connected’, be it in the provision of services or added functionality for consumers (smart heating, lighting, security, IoT appliances, IFTTT), metering and billing by utility providers (smart meters and monitoring) or in the wider supply chain’s pursuit of ever more detailed performance and condition monitoring and management (sensors, actuators, asset alerting). The aim of this project is to test the feasibility of bringing all of this data together to a central model that gives the data and insight not only some context but which also allows for the generation of meaningful actionable advice for householders, building and asset portfolio managers and also the wider supply chain. What will be developed and tested is the concept of a central open platform that can interpret and provide feedback and actionable advice from the wide range of data generated in the build, construction and most importantly, use, of buildings. Timing of this is particularly pertinent given that building users are becoming increasingly empowered with regards to how they use and understand buildings; putting asset managers and the wider supply chain at risk of being left out of touch and behind the curve.