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As contractors become more and more squeezed, the situation has reinforced calls for a smarter way to construct builds.
On top of this, the UK government has put infrastructure at the heart of its post-Covid-19 Build Back Better plan and has ambitions to become net-zero by 2050. Building houses, offices and transport systems that are energy efficient will be an essential part of cities that are sustainable.
In order for this lofty goal to be realised, the construction industry needs to undergo a transformation. The problem, however, is that the industry has been something of a laggard when it comes to embracing technology or innovation. As Paul Dunn, executive director of architecture company CallisonRTKL, said: “If the industry is going to achieve transformational change it must go further in embracing digital, advanced materials and new technologies.”
A report published last December by the Institution of Civil Engineers (ICE), A Systems Approach to Infrastructure Delivery, pointed to a lack of resources and a knowledge and skills gap among stakeholders on the lower rungs of the construction supply chain ladder as a big barrier that is holding the industry back from exploiting the full potential of data.
Cameras and sensors
Given these stumbling blocks, other stakeholders further up the supply chain often find that the flow of data through projects is slow or disrupted. Among those industry stakeholders the ICE spoke to in researching the report, an oft-cited complaint was that disruption meant risks, including structural or engineering issues, are identified later than would be ideal. This means that problems are often dealt with only once they’ve occurred.
One solution put forward by the ICE report is ‘automate, automate, automate’. A combination of a network of cameras and sensors to various assets, machinery and plant, and using digital-twin technology to monitor the performance and health of a building or structure, supports the creation of a connected environment in which industry stakeholders are empowered to make smarter decisions.
Aside from the logistical challenges posed by adding cameras and sensors to machinery and plant, there’s a question around data quality. Data collation and basic analysis is great, but how stakeholders are able to harness it could be improved through the application of artificial intelligence (AI) and machine learning.
A collaboration between the contractor Winvic and the Big Data Laboratory at the University of the West of England at Bristol is one example of how AI and machine learning are being put to work.
There are plenty of occupational hazards on a construction site and risk management is critical. The collaboration hopes to help the industry build more safely by developing a realtime warning system that alerts those on site to potential dangers and structural issues.
A network of cameras uses AI to detect hazards and, as the system collates more data, machine learning enables it to make better predictions, reducing health and safety issues. For example, such a system could predict faults with ventilation systems that extract fumes and dust – respiratory protection has become even more important during the pandemic.
Winvic and the university’s researchers are also exploring how AI and machine learning can be used to reduce embodied carbon in the planning and construction phases of a build.
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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.