This infrastructure gap – the difference between funding and construction – is vast. And while governments and firms in all places are feeling the strain of constructing an energy efficient and sustainable built environment, it’s proving greater than humans can do alone. To redress this imbalance, many organizations are turning to numerous types of AI, including large language models (LLMs) and machine learning (ML). Collectively, they should not yet capable of fix all current infrastructure problems but they’re already helping to cut back costs, risks, and increase efficiency.
Overcoming resource constraints
A shortage of expert engineering and construction labor is a serious problem. Within the US, it’s estimated that there can be a 33% shortfall in the provision of recent talent by 2031, with unfilled positions in software, industrial, civil and electrical engineering. Germany reported a shortage of 320,000 science, technology, engineering, and arithmetic (STEM) specialists in 2022 and one other engineering powerhouse, Japan, has forecast a deficit of greater than 700,000 engineers by 2030. Considering the duration of most engineering projects (repairing a broken gas pipeline for instance, can take a long time), the demand for qualified engineers will only proceed to outstrip supply unless something is finished.
Immigration and visa restrictions for international engineering students, and a scarcity of retention in formative STEM jobs, exert additional constraints. Plus, there may be the problem of task duplication which is something AI can do with ease.
Julien Moutte, CTO of Bentley Systems explains, “There’s a large amount of labor that engineers have to try this is tedious and repetitive. Between 30% to 50% of their time is spent just compressing 3D models into 2D PDF formats. If that work will be done by AI-powered tools, they will get better half their working time which could then be invested in performing higher value tasks.”
With guidance, AI can automate the identical drawings a whole bunch of times. Training engineers to ask the appropriate questions and use AI optimally will ease the burden and stress of repetition.
Nevertheless, this is just not without challenges. Users of ChatGPT, or other LLMs, know the pitfalls of AI hallucinations, where the model can logically predict a sequence of words but without contextual understanding of what the words mean. This could result in nonsensical outputs, but in engineering, hallucinations can sometimes be altogether more dangerous. “If a suggestion was made by AI, it must be validated,” says Moutte. “Is that suggestion protected? Does it respect the laws of physics? And it’s a waste of time for the engineers to must review all this stuff.”
But this will be offset by having existing company tools and products running simulations and validating the designs using established engineering rules and design codes which again relieves the burden of getting the engineers having to do the validating themselves.
Improving resource efficiency
An estimated 30% of constructing materials, similar to steel and concrete, are wasted on a typical construction site in america and United Kingdom, with the bulk ending up in landfills, although countries similar to Germany and The Netherlands have recently implemented recycling measures. This, and the rising cost of raw materials, is putting pressure on corporations to consider solutions to enhance construction efficiency and sustainability.
