Safety leaders at construction firms often promote vigilance on projects, encouraging workers to be a set of eyes for their fellow coworkers. But there are limits to the observations that employees can make as they go about their tasks for the day.
Artificial intelligence tool Newmetrix marries machine learning to jobsite photos, videos and other sources and rakes through the data far more quickly than a human could, so that fewer risks to workers slip under the radar. A growing number of general contractors are exploring the product’s ability to identify and label risks such as standing water or missing personal protective equipment like hard hats, safety glasses, gloves and more.
The nature of machine learning means that the more data is fed into the tool, the more the algorithm, dubbed “Vinnie,” can advance in its accuracy. Following up on a study which found that Vinnie learned from Suffolk’s data to predict roughly one in five safety incidents with 81% accuracy, nine construction firmsjoined a data-sharing councilin March to further accelerate the tool: Suffolk, Barton Malow, Clayco, DPR Construction, JE Dunn, Messer Construction Co., Mortenson, Shawmut Design and Construction and the Bouygues Group.