Examples of artificial intelligence (AI) in construction are increasingly common throughout the industry, from planning to building.
Construction professionals are turning to AI to add efficiency and accuracy to projects, monitor and track equipment usage and location, and a long list of other AI-driven applications.
Research and Markets reports that construction-related activities bring in over $10 trillion every year, a figure expected to grow by 4.2% through 2023. AI is helping to grow the industry.
In its article, “Artificial intelligence: Construction technology’s next frontier,” McKinsey & Company identifies AI as a critical component of modern engineering and construction approaches. AI technologies, the firm reports, help these industries solve some of their greatest challenges, including cost and schedule overruns and safety concerns.
AI can be found in construction throughout project life cycles — including inception and design, bidding, financing, transportation management — and operations and asset management.
Here, we take a look at some ways AI in being used in construction:
See more: Artificial Intelligence Market
5 examples of AI in construction
1. Generative design is improving the way teams create 3D models
Over the past several years, building information modeling (BIM) has emerged as a new (and better) way to create the 3D models that construction professionals rely on to accurately design, build, and repair. Today, BIM platform programmers are improving the capabilities of BIM with smart, AI-driven features.
BIM applies various types of tools and technology, including machine learning (ML), to help teams avoid a common, costly issue: duplicated work. All too often, sub-teams working on shared projects spend time creating models that other sub-teams have already created. With BIM, users “teach” machines to use algorithms to generate designs with multiple variations. As the AI builds models, it learns from each iteration until it comes up with the ideal model.
BIM adoption is on the rise. A recent Dodge Data and Autodesk study, “Accelerating Digital Transformation Through BIM SmartMarket Report,” concluded that BIM is at the heart of a trend toward increased digitalization in the construction sector. The study found that nearly half (47%) of “high-intensity” construction-focused BIM users are close to achieving their overarching digital transformation goals.
2. AI can help construction managers measure site progress
Companies like Droxel are using AI solutions that track progress on construction projects and provide real-time measurements of quality and project progress. Droxel creates robots equipped with cameras that can autonomously move through the construction site to capture 3D “point clouds.” Effectively, the company says, the AI-powered robots create digital twins of job sites.
Once a complete digital model is in place, Doxel uses a neural network to process project data and cross-references it against information from the BIM and bill of materials. The generated information is helpful for project managers, especially those who need to keep track of large-scale projects with thousands of moving parts. These insights include:
- Financial information such as how much is owed or whether there are signs the budget is in jeopardy
- How close to schedule projects really are
- Identifying quality errors in their early stages, when they can still be addressed and mitigated
3. IoT is powering robust fleet management
The Internet of Things (IoT) is transforming the way construction companies manage fleets of equipment and vehicles. IoT solutions include location awareness, predictive maintenance capabilities, fuel and battery consumption, and more through AI-fueled telemetrics.
IoT devices and tags deliver vital information from the field. They can be attached to equipment and inside shipping crates, semi trailers, tools, and virtually anything else construction teams need to track. Being able to predict when a piece of equipment is about to break down is an invaluable, time- and money-saving benefit in the construction field.
4. AI is creating safer job sites
According to OSHA, construction workers are killed on the job five times more often than other laborers. Those accidents include falls, being struck by an object, electrocution, and “caught-in/between” incidents where workers are squeezed, caught, crushed, pinched, or compressed between multiple objects on the job site, including equipment.
Platforms like Newmetrix are improving job-site safety conditions through machine learning that can identify risks before accidents happen or analyze sites after incidents have occurred.
The software can monitor sources like photos and video and then apply predictive analytics to flag potential problems so site managers can address them right away. Users can interact with a centralized dashboard to generate reports that rank projects on potential safety risks, such as unsafe scaffolding, standing water, and workers missing personal protective equipment like gloves, safety glasses, and hard hats.
5. AI is mitigating construction labor shortages
McKinsey & company reported in 2017 that construction firms could increase their productivity by as much as 50 percent through AI-enhanced analytics. This is great news for construction companies who can’t locate enough human workers to round out their jobs sites, and those companies are the rule rather than the exception.
Not only is it challenging to find laborers — CNN reports that the industry is facing a shortage of more than one million workers in the U.S. — it’s difficult to retain workers, as well. According to the Bureau of Labor Statistics, the average monthly turnover rate in the construction field hovers above 5%, compared to less than 4% across all industries.
AI-powered robots like Boston Dynamics’ Spot the Dog empower project managers to determine, in real-time, conditions about multiple job sites, including whether they should shift workers to different parts of projects or to new project sites altogether. The robot “dogs” monitor sites when work is happening and after hours to pinpoint pain points.