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

Company:

BIM ACADEMY (ENTERPRISES) LIMITED, BUILDSTREAM LIMITED, Northumbria University, WALTERS PLANT HIRE LIMITED

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 realtime
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).