A new playbook from the Chartered Institute of Building aims to tackle the fears and fictions about AI adoption in the infrastructure sector by providing the facts.
In the 1970s a technological development wrought fear amongst parents and educators. The calculator had arrived.
How would students grasp basic numeracy concepts if a tool did the hard work for them? Should its use be allowed in tests? What would its impact be?
Ultimately, it transpired that a grasp of core mathematical processes was still key to maths success. But the calculator could do correct calculations at speed, supporting efficient, productive working.
Today, AI is the technology generating, in some quarters at least, fear and panic. The Economist noted last year that global Google searches for “is my job safe?” had doubled in the previous months amid fears of large language models rendering human skills and experience redundant.
Now, for infrastructure professionals, a new guide aims to replace AI fears and fiction with facts. The AI Playbook, produced by the UK’s Chartered Institute of Building (CIOB), provides details of the benefits that AI brings specifically to the infrastructure and construction sectors as well as practical steps individual organisations in the sector can take to generate meaningful benefits from the technology.
Dave Philp, Cohesive’s Chief Value Officer, is also chair of the CIOB’s Digital and Innovation Advisory Panel. In that role, he led the development of the playbook.
“The reality is (that) the construction industry is in the age of AI whether we recognise it or not,” explains Dave. “Understanding AI in the context of our sector is vitally important to help determine and shape how it might usher in new opportunities.”
What’s in the playbook?
In a forward to the playbook, Caroline Gumble, CEO of the CIOB, which has more than 35,000 members across the UK, says that AI has “the potential to transform our industry.”
Its stance is this: That the value of tools including Building Information Modelling (BIM) and digital twins is multiplied many times when they are powered by AI and its ability to unlock the full value of construction data to provide insights, predict and automate.
“What is striking is the symbiotic relationship between BIM, Digital Twin, and AI, where each strengthens the others, amplifying their effectiveness when integrated.”
Central to their success it says, is the availability of high-quality, structured data.
The playbook acknowledges that AI and its large language models are not new concepts. Unlike other technologies though, it says, the big difference is the pace at which AI is moving.
Contributions include one from Murillo Piazzi and Paul Thorpe from the BIM Academy, on the practical implementation of AI (Chapter 1 p14). It emphasises the need to “evaluate and validate the AI’s output”, which is most easily done with tasks you are familiar with so can discern between satisfactory and unsatisfactory results.
Vicki Reynolds, Chief Technology Officer, Catalyst & Obi, writes about the importance of the data quality (Chapter 5, p36). “Although not always considered the most exciting piece of the technology puzzle, good data governance strategies are a critical part of any business case for AI,” she writes. “Robust and effective data governance will also have a profound knock-on effect across an organisation. If data is well organised and trustworthy, then businesses will be able to analyse that data with higher precision and clearer insight across the board.”
She advises that an AI strategy should “form part of an overall information management plan from the earliest possible stage of a project or build.”
May Winfield, Global Director of Commercial, Legal & Digital Risks at Buro Happold, sets out some of the legal issues around AI (Chapter 6, p 40). Her chapter includes a cautionary story about AI “hallucinations” where generative AI can appear to completely fabricate results. “Generative AI is, like a calculator, a tool at the hands of a user to facilitate and speed up results,” she writes. “It is not a panacea to provide complete automation of tasks that require analysis.”
Cohesive’s Dave Philp and Stefan Mordue, of Bentley Systems, write about digital twins and AI (Chapter 7 page 45). They explain that AI algorithms can continuously learn from the data collected by digital twins, improving their accuracy and predictive capabilities over time. “By leveraging AI, digital twins can deliver more accurate insights and predictive capabilities, thereby enhancing their value to businesses,” they write.
They provide examples of specific ways in which AI can enhance a twin’s capabilities and support more effective project planning, quality control, maintenance and sustainability. These include:
1.Improved energy management:
Digital twin: Monitors energy consumption patterns in realtime.
Integrated with AI: Optimises energy use by adjusting systems dynamically.
2.Improved Lifecycle management:
Digital twin: Tracks the entire lifecycle of building components.
Integrated with AI: Provides insights into the optimal timing for upgrades or replacements.
3.Improved structural health monitoring.
Digital twin: Monitors the structural integrity of infrastructure in real-time, capturing data from embedded sensors.
Integrated with AI: Analyses this data to detect signs of structural stress, enabling early intervention.
The playbook also includes, at its end (p58), a list of useful resources and links. To download the playbook and read it in full, click here.
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