In recent years, more and more construction companies have started to utilize artificial intelligence (AI) and machine learning in various ways to optimize efficiency, reduce costs, mitigate risks and improve safety. We often hear the terms used interchangeably and while they are closely linked, they’re not quite the same thing. Let’s begin by defining and differentiating the two concepts, and then we’ll discuss how their applications are already being used to transform the construction industry as we know it.
What is Artificial Intelligence (AI)?
According to McKinsey & Company, artificial intelligence (AI) is typically defined as “the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity. Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.”
Simply put, AI is used to describe technology that is capable of performing, or augmenting, cognitive tasks that once had to be done by humans.
What is Machine Learning?
Machine learning is a type of AI in which “algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.” (McKinsey & Company)
The four types of machine learning are: supervised learning, unsupervised learning, reinforcement learning, and semi-supervised machine learning.
Examples of AI & Machine Learning in Construction
The possibilities of utilizing AI and machine learning in construction are vast, as are the benefits. Below are five ways construction companies are already implementing these technologies to improve processes and recognizing the benefits:
1. Increased productivity on job sites
Self-driving construction machinery is being used to perform necessary but repetitive tasks like bricklaying, concrete pouring, welding and demolition in lieu of human labor given the technology’s ability to complete the tasks quicker and more efficiently. Autonomous or semi-autonomous bulldozers can be programmed to exact specifications to perform excavation and prep work needed to get a site ready for construction. Project managers are also able to track progress in real-time using cameras, facial recognition software and other technology to assess productivity and adherence to protocol.
2. More efficient project planning
Some companies are already using AI-powered robots to capture 3D scans of construction sites, which are then uploaded into a deep neural network that can classify the status of various sub-projects. This allows for early intervention if something seems off-track, so the team can handle small issues before they turn into more complex problems.
3. Risk mitigation
Every project phase has inherent risks associated with quality, safety, time or cost. Today there are AI and machine learning solutions that can be used to assess risk and automatically assign priority to complex issues or areas most prone to threats. Time and resources are limited, and this helps project teams understand where to focus their time and effort in order to mitigate the most high-risk situations.
4. Prevent budget overrun
Even with careful planning and skilled project teams, it’s not uncommon for projects to run over budget given the number of variables that can change and directly impact costs or the schedule. Artificial neural networks—brain-inspired systems intended to replicate the way humans learn—can be employed to predict potential budget pitfalls by assessing factors like project size, contract type and performance of team members, as well as analyzing other patterns too complex for the human mind, to reduce the likelihood of budget overruns.
5. Improved worker safety
The construction industry is witnessing a rise of machines and sensors that are capable of increasing jobsite safety and reducing risk—for example, sensors that can detect worksite weather conditions to assess risk factors. These tools, when combined with AI, are able to intelligently monitor job sites by analyzing data in real-time to predict the likelihood of problems or malfunctions that may occur and translating that data into actionable insights. AI-enabled surveillance systems with facial and object recognition software also make it possible to detect unsafe behavior and alert team members of potential hazards.
A Look to the Future
According to a recent Research Dive report, “the artificial intelligence in construction market size was $408.1 Million by end of 2018, and is anticipated to reach $ 2,642.4 Million by 2026, growing with a CAGR of 26.3% during the forecast period.” The continued growth will largely be driven by the cost efficiency and availability of advanced AI products, as well as the ability to more accurately predict needs, assess risks and improve safety.
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Kathryn Dressler is a content strategist with more than 10 years of experience across the spectrum of marketing services, including blogging, social media, public relations, copywriting and editorial services.