An innovative feasibility study into optimising equipment use in construction with BIM, IoT and data analytics.
BIM Academy is undertaking an exciting new research project in collaboration with BuildStream (formerly GearBuddy), Costain, Northumbria University and Lynch (Plant Hire), exploring how we can improve the utilisation of plant and equipment on site. This is through the collation and aggregation of onsite data, combined with a construction programme and a 3D model, allowing for a better understanding of current utilisation levels and where improvements could be made.
The consortium includes five partners that represent the entire value chain required to deliver and test this solution and aims to reduce equipment fleet costs and carbon emissions, and improve its productivity (improved planning and understanding of equipment performance).
Growth in annual productivity with construction has increased by only one percent in over 20 years [McKinsey Construction Productivity Report, 2017]. There is a clear need for current (and past) ways of working to be challenged in order to combat and address this. A key contributor to this performance gap is associated with the management and operation of the plant and site equipment fleet, with it accounting for ten percent (public building project) to 60 percent (major infrastructure projects) of total project cost [Costain data]. Current management and operation of plant/site equipment fleet also contribute significantly to project delays, on/offsite congestion and air pollution (eg up to seven percent of London’s NOx emissions).
With effective management of plant and equipment, several opportunities arise, including productivity gains (for clients, contractors, subcontractors and plant hire companies) and the potential for decreased pollution and noise, thus providing an opportunity to minimise the impact on the environment and people. Research carried out by consortium partners show that plant/equipment utilisation rates on site can be as low as 30 percent, there is up to five times equipment duplication, there is a large crossover of equipment requirements across work packages, and issues with site congestion that can lead to health and safety risks.
Despite the impacts of ineffective management of plant and equipment highlighted above, equipment fleets are still a major blind spot within construction due to a lack of data and ways in which to digest and view it. Despite an increase in the data made available via telematics (generally restricted to heavy equipment, typically equal to or over the weight of ten tons), most contractors, subcontractors and specialists still only record data sporadically and lack effective systems and ways of capturing, storing and leveraging the data to aid with management, planning and maintenance. Existing telematics systems are one dimensional (eg recording operational data such as working hours, fuel consumption, fault code) and offer little context in the data offered.
Earlier work by the consortium at the HS2 project confirmed demand for systems looking at equipment fleet management (ie estimation/selection, deployment, coordination, and visualisation). The project seeks to establish our position as one of the first and leading tech platforms combining IoT, BIM and data analytics.
The understanding of equipment performance, the data captured, and the predictive capabilities will all help contractors and the supply chain to develop more competitive and accurate bids in the future.
The consortium members will aim to build upon their current expertise and experience during the project to contribute to and develop the project outputs and deliverables.
BuildStream will extend functionalities of their equipment visualisation dashboard (currently in beta stage – see screenshot below) and benefit from a live case study which will help the product launch into the market.
BIM Academy will test novel services, including live and connected 4D BIM plans for site management, for clients in the UK and worldwide utilising Synchro’s 4D construction project management software. This area is expected to grow as the smart connected construction site increases in its popularity. BIM Academy will also lead the integration work with BIM models and 4D BIM programmes and analyse the correlations between project attributes and equipment demand. The 4D BIM model will be used to correlate which equipment is active on specific tasks within the project (for example, how many excavators are working on digging a hole, and how many access platforms are working on installing cladding, etc).
UNN team will contribute to exploring machine learning to infer knowledge from historical and real-time data sets to inform decisions about site equipment logistics and operation. UNN will also help in the development of the live equipment dashboard and support BuildStream with the integration of the ML solution into dashboard platform both with the back end and front end functionality.
Costain will provide a BIM model which will be enhanced by BIMA and linked by BuildStream to their proprietary platform which will provide historic data, and enable access to/engagement with their supply chain and take part into the testing/demonstration.
Lynch Hire will be at the forefront of the new digital age for construction equipment by digitising machines in the proposed ecosystems. Lynch will also participate in the demonstration and provide historic data from their existing equipment telematics, enabling access to useful, reliable data analytics, in one consolidated platform.
The project, by delivering the proposed platform, will provide a unique use case exemplifying a move to the digital construction site of the future. IoT hardware development will be led by BuildStream who are supported by the MINI/BMW technology team.
The project will use HS2 enabling works as a pilot test site, to allow the team to determine use cases to add real value to a live project. We look forward to collaborating with the site team with regular visits, updating on progress throughout. Further blog posts will follow each quarter.