HOW NEXT-GEN AI COULD CHANGE CONSTRUCTION BY MARY LOU JAY
The introduction of the generative artificial intelligence (Al) technology ChatGPT late last year stimulated a great deal of discussion in 2023 about how Al will change the workplace. Generative Al can "think" like a human and sift through data to produce essays, write computer code and answer questions in a conversational way. Is there some way that construction companies can harness those new capabilities to improve the efficiency and quality of their operations? There are several types of Al technology, and construction companies have been trying out some of these variations. Contractors have successfully used robots to lay bricks for building projects and employed excavators that operate autonomously to prepare the ground for infrastructure jobs. These construction tech pioneers understand that such technology may increase job site efficiency and help alleviate the labor shortage. For example, enabling experienced heavy equipment operators to guide excavators' or loaders' operations from thousands of miles away could enable companies to retain workers who are reluctant to travel or who are no longer physically able to sit in a truck all day. However, there are still significant barriers to widespread adoption of such technologies. Brianne Stewart, construction technology manager at Milwaukee Tool Company, thinks that robots will gain wider acceptance when there is a convergence of enabling technologies that allow them to operate in the continually changing environment of a job site. Those technologies include Al, better environmental capture systems like vision and LIDAR (laser-based light detection and ranging system), and increased processing capability. "You need the technology that captures information about the environment, and you need the processing capability to understand it and make decisions," she said. Since these technologies usually require connectivity to
some platform via Wi-Fi or a consistent cellular connection, a lack of consistent connectivity coverage on many job sites is another problem. Al is making an impact on job sites on a smaller scale, however, as tool manufacturers incorporate it into their products. Using machine learning (a subset of Al) has enabled Milwaukee Tool to improve its tool-locating capabilities. "We get a lot of location updates within our system, and we can use machine learning to pick which one is the most accurate," said Stewart. That saves contractors time looking for tools — one cause of downtime on a job site. Milwaukee recently introduced a controlled torque impact wrench that can be calibrated using a subset of Al, machine learning. The wrench is calibrated at the start of a project, considering data variables like material, temperature and bolt type. The algorithm in the tool then determines how to best install the bolt in a repeatable way that meets the quality standards of the solar manufacturer. This will provide substantial productivity improvements by enabling contractors to merge two steps into one. Contractors can now use a single tool, the impact wrench, to install a solar panel, rather than the current standard of an impact wrench followed by a torque wrench, Stewart added. The company has also used machine learning to develop its algorithms for AutoStop™, which automatically shuts off the tool when it detects a kickback. "By using machine learning in our development process, we have made this feature extremely accurate. So, there is less of it stopping when you do not want it to stop, and it stops a lot quicker when it should stop due to a safety concern," she said.
LEVERAGING CONSTRUCTION DATA
"Most people get into construction because they want to solve challenging problems in the physical world and build things that matter, but too often, construction professionals get buried in mountains of paperwork and button pushing on computers. this leads to a high rate of burnout and helps drive the labor shortage the industry is facing."
Although Al may not affect field operations on a large scale in the immediate future, it is likely to have a bigger impact on the management side of the construction business. "I think we will see the widest adoption of Al in areas of construction where large datasets exist," said Stewart. "I see it as an augmentation and improvement on existing capabilities. Estimating, project scheduling, costs, labor planning - that is where you can use that historical data in order to understand it better and make predictions about the future."
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