Imagine a jobsite tool that only improves with time. Imagine a tool that records each and every change order, every bit of rework and every safety incident. Not only does that tool record it, but it also files it away for future projects. Imagine a tool that learns from the past to improve your company’s future.
Through artificial intelligence and machine learning, several software providers in the construction industry are building technology to do all of the above. Pat Keaney, director of product management for Autodesk’s BIM 360 suite, has been working on such a solution for several years.
Keaney is part of a team called Project IQ. The team is comprised of approximately 20 artificial intelligence (AI) experts, data scientists, product engineers, user experience (UX) designers and several more roles, all of whom are focused on taking the mountains of data that circulate a construction site on a daily basis and making that data actionable for the project leader or business owner. The team has been collecting data from a number of different companies involved in Project IQ’s pilot program. The team has used insights from that data to test and improve several different software solutions. The basis of this solution evolves around machine learning.
For the team at Autodesk, it was immediately evident that for Project IQ to succeed, the general contractors would have to be closely involved with the process from start to finish, because it is their companies’ data from which the the team would be learning. And because the team wanted to make sure that whatever it developed would need to be as accurate and helpful to the industry as possible.
Project IQ works with 20 of the largest general contractors in the world, primarily in the United States, but the list includes one in the United Kingdom and one in New Zealand.
The first rollout worked to more accurately capture problems as they occur on the jobsite, as well as track and close out those problems in a more efficient manner.
“You can literally have thousands of open issues on a project at any given time,” Keaney said. “For example, one project manager told me that he could literally spend all of his time staring at issues and trying to find solutions for them.”
One company involved in the initial rollout for Project IQ was Utah-based general contractor Layton Construction. In the last decade, Layton Construction has grown from being a regional contractor to a company that plays on a national level. Rick Holbrook, director of construction operations, is responsible for managing the company’s many business units and project segments. More recently, he has also been tasked with improving the company’s quality program, which is how Layton connected with the team at Autodesk.
“Our executives had enough foresight to see that Autodesk solutions fit well with Layton’s processes. We are a process-driven company that focuses on quality, unity, truth and completeness. Tech is the best tool we have in our arsenal to further those goals,” Holbrook said. “Instead of digging through archives and attempting to analyze all of that data, Project IQ software does it for us.”
The drawback, though, is getting the entire team on board to input good, consistent data. Older solutions were not conducive to easy data compilation, so Layton and the Project IQ team worked together to figure out a software solution that was accessible to the contractor, at any time, in any place.
After the data for each issue is gathered by the team in the field, the IQ team can then cull through all of it. The tech categorizes each issue as either a high or low risk. The team can also auto-categorize issues into types that are inherently more risky. For example, any element of a project that involves water integration would fall into the riskier category, simply because it is a more complex build. This auto-categorization allows a project manager or superintendent on the jobsite to come in each morning, check his/her phone and immediately see a breakdown of the high-risk and low-risk issues on each jobsite. This helps the entire team immensely simply because it prioritizes the issues that need to be resolved first.
Most of the time, five to seven of the issues need a project manager’s immediate attention, Keaney said, but the software helps separate the immediate risks from the ones that are not quite so dangerous. With project risks neatly in check, the Project IQ team has turned its sights on alleviating another recurring jobsite challenge: safety.
“The idea was, if the software could learn to separate high risks from low risks on the jobsite, it could also record and sift through safety incidents,” Keaney said. For example, fall risks are common on every jobsite. A superintendent or safety director should already be paying attention to any kind of behaviors that result in a fall risk. Keeping detailed safety records is already commonplace on jobsites across the U.S.
However, these project records often go into a database and are never viewed again, barring litigation or an OSHA citation. With machine learning, safety directors can effectively sift through the data, and get a quick, all-encompassing look at which project partners, such as project owners and subcontractors, are more prone to creating situations in which fall risks arise. The software looks for patterns of behavior on a project—from project patterns to subcontractor behaviors. There are 35 main safety hazards the software looks for in every recorded safety incident.
“What’s been really interesting is to see the different patterns we see on different jobsites,” Keaney said. “The safety managers we work with are excited about that because it means they can customize the discussions they are having surrounding safety on the jobsite.”
For the team at Layton, a machine-learning solution focused on safety had immediate results.
“One way we were immediately able to improve processes was by automating and standardizing certain safety checklists on all of our projects,” Holbrook said. “The software gathers all the