Machine learning is helping construction companies the world over to replace monotonous human tasks. Machine learning also helps with the designing and planning of projects, and it enables teams and companies to make better-informed decisions for a more streamlined workflow. Indeed, there are countless useful applications of machine learning in the construction industry. Let’s take a closer look at some of the primary applications.

What is machine learning?

Machine learning is a subset of artificial intelligence that involves machines learning and predicting outcomes by themselves. The machines do not require a person to program them. Instead, the machines use software and algorithms to enable them to make predictions based on the analysis of data. For instance, a machine could inform you that it requires preventative servicing.

There are traditionally three primary machine learning approaches. They are:

· Supervised learning, in which a computer is presented with input examples and desired outputs. The computer then learns a general rule to map those inputs and outputs.

· Unsupervised learning, in which the learning algorithm does not receive any labels and is instead left by itself to discover structure in its input.

· Reinforcement learning, in which a program interacts with a dynamic environment to perform a specific goal.

What industries use machine learning?

Today, many different industries use machine learning. For example:

· In marketing, machine learning helps marketers to create better customer segmentations, deliver more relevant campaigns, and measure performance more effectively.

· Online casinos use machine learning to analyse players’ betting data and protect players by promoting responsible gambling. Machine learning also enables the online casino industry to have greater stability and improved planning.

· The healthcare industry is increasingly using machine learning in various ways, such as with wearable devices and sensors that give healthcare professionals access to patient health in real-time.

Now, let’s take a look at the useful applications of machine learning in the construction industry.

Machine Learning Can Enhance Design Quality

When machine learning is utilised during the design phase of a construction project, it can improve the quality of designs. For instance, if you are building meeting rooms for a company, machine learning can tell you how the space can match the people using it by predicting things like how frequently the rooms will be used. Machine learning can also help to highlight mistakes and omissions in designs before going ahead with construction. You can also test various environmental conditions and scenarios in the model.

Machine Learning Can Increase a Project’s Lifecycle

Generally, there are gaps in facility management’s information, making it challenging to manage repairs and renovations on-site cost-effectively. Machine learning can be instrumental in facility management and extending an asset’s total lifecycle. It can streamline processes via collecting and utilising data better. For instance, machine learning can classify documents and data such as work orders and assess conditions in real-time with extreme accuracy. That helps to speed up processes no end. And when machine learning is integrated with a BIM model in maintenance and operations, it can determine the best way to conduct maintenance and repairs by visualising where and when issues will occur.

Machine Learning Can Create a Safer Construction Site

Safety is, of course, paramount to any construction site, and machine learning provides ways to ensure job sites are as safe as possible. For example, machine learning tools can identify when a person is not wearing a hard hat much more quickly and accurately than humans can. Because machine learning learns and adapts over time, it helps humans to find problems and solve them efficiently. Furthermore, machine learning can determine risks before they even happen. It can identify risks, measure the impact of those risks and use predictive analytics to reduce the risks. And with the assistance of artificial intelligence algorithms, risks can be comprehended better, and problems can be prioritised automatically. With such tools available, construction managers can not only prevent problems. They can also streamline workflows for a more efficient process.