The startup, which uses machine learning to predict environmental impacts during engineering and operational works, has received funding from government initiative Innovate UK to work on the planned high speed railway.
Qflow won £260,000 as part of a competition ran by the Small Business Research Initiative, or SBRI, to develop demonstrators to enhance resilience, freight operations and the environment.
The project will be delivered in partnership between Qflow and a Skanska Costain Strabag joint venture, with the high speed rail network acting as the project demonstrator.
Brittany Harris, co-found of Qflow said: “This funding enables us to grow our team and accelerate our product development for launch on upcoming projects. Building a sustainable future is a deep passion of ours, and I can’t wait to engage more of the industry in doing just that.”
Qflow will focus on creating a real-time platform for gathering environmental data, such as flood risk, water consumption and more, and analysing this against programme activities to predict upcoming exceedances.
Environments around railway works, stations and depots are often at risk of dangerous air quality levels, noise pollution, inefficient resource consumption and local disruption to passengers and stakeholders.
The southern sections on the new HS2 route, between Euston station and West Ruislip will be used to demonstrate how machine learning can be applied to predict critical environmental risks; to enable a safer, cleaner way of working and preventing disruption to local communities and passengers.
Qflow has previously taken part in trials on construction sites, and has been awarded by multiple industry bodies including the Royal Academy of Engineering, London Waste and Recycling Board, and the Mayor of London’s Clean Tech campaign.