An important conversation that has been popular for quite some time now is the introduction of Artificial Intelligences. The AIs have slowly been incorporated in many different industries, and have been a great addition to the workforce.
Recently though, artificial intelligence and civil engineering have also started mixing. The combination is a long-awaited one, and although it may take some time before AIs start seriously working on sites, the progress is looking good.
Surprisingly enough, in a world where everything seems to have evolved drastically, civil engineering and its industry are one of the few that have kept their roots. Probably because there isn’t much that can be evolved when it comes to pouring concrete or bricklaying, the mechanisms have stayed the same. The consulting group McKinsey and Company have studied the phenomenon and have reported that although the industry is worth more than $10 trillion yearly, the investments in technological advancements are around 1%. When compared to other industries the 1% is severely less than other, and puts the civil engineering world way behind others.
Nevertheless, McKinsey and Company are optimistic about the AIs pushing their way through to the Civil Engineering field. In fact contractors, service providers, owners, and even operators cannot ignore the artificial intelligence knocking on their door anymore. And because the knocking is most likely being done by a robotic hand, the AI influence on the construction sites will not be the same. There will be no robots laying bricks (yet) but rather more of an overview in terms of algorithms and predictions.
Companies already in the process of applying artificial intelligence have been shown to be more likely to profit from using the AI by 50%. And because the AIs can work technology wonders, five different kinds of applications have been developed. These applications mostly focus on algorithms, but will most likely save time and money when applied to certain scenarios.
The first application is known as “reinforcement learning”, and is when trial and error are studied through algorithms in order to understand what the best way of doing something is. This technique can come in handy when planning and/or scheduling are being dealt with. The second is called “predictive applications” and is when predictions are made to forecast various risk scenarios. The third application is “supervised learning applications for modularisation and prefabrication” which entails reports on the supply chain organization. The fourth is known as “machine learning” which, like the title implies, is a machine capable of learning certain movements and be able to prefabricate materials, and/or carry out maintenance tasks.
And last but not least, the fifth applications is “image recognition” – a technique that focuses on the use of drones and the images they collect. The images will be able to assess quality control, and give the constructors a more accurate image of where the project is going, and if changes need to be applied beforehand. A real lifesaver seeing how much time it could save!
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