Artificial Intelligence (AI) has changed the way companies update, monitor, and build infrastructure. In the damage prevention sector, innovations like 24-hour monitoring systems, gas leak detection technology, and improvements in safety gear have also made worksites and infrastructure projects safer.
Often overlooked in the damage prevention field is the application of AI, which be used for tuning ticket processing, monitoring multiple sources of information, building statistical safety models, and speeding up on-site repairs to damaged utilities. When AI is used for things such as 811 call centers and on-site workers, it has the potential to reduce damages, identify damage patterns, and shrink excavator damages overall.
The ticket processing system at many call centers can easily move from a trickle to a torrent, due to the number of tickets submitted daily. The 2019 DIRT Report highlights an AI that scans tickets for factors that determine if a ticket has a higher risk for damage.
This predictive algorithm, Urbinit, allows One-Call centers to classify high risk for damage jobs based on the type of utilities around a job site and the record of the excavators attempting to carry out the job. It also allows One-Call centers to include a risk score for the particular site based on the algorithms processing of those data points. This helps identify high priority tickets and reduces the flow of tickets that are processed at call centers. In New York, where Urbinit is employed, the AI helped damages fall statewide, as only a small percentage of tickets account for the largest number of damages.
Similar AI’s have been utilized to improve workplace safety using the same type of data processing to identify hazards at a specific site. Urbinits Lens for Worker Safety processes data that includes weather conditions, worksites, schedules, leading indicators of potential hazards, and any historical incidents that might have occurred in the area to give supervisors a comprehensive picture of an area.
Other AI like the AWS Panorama DeepLens can be paired with video cameras to improve quality control, supply chain efficiency, and map out routes vehicles use to deliver products to customers. This same monitoring system can be employed in manufacturing centers or warehouses as well, with some AI’s being able to simultaneously detect overheating machines, the probability of vehicles colliding, and alerting workers if they are in a statistically unsafe area due to high foot or vehicle traffic.
AI focused on locating sources of damage if a utility line is hit can also be crucial in speeding up repair jobs and ensuring repair crews are safe. A prototype AI from Blackline Vision can pinpoint gas leaks, allow companies to continuously monitor underground gas lines, and alert operators if a potential repair is needed. Most industries rely on alarms or manual assessments to locate areas in need of repair, however, AI will identify leaks early and eliminate the need for any additional equipment for identifying leaks. By acquiring data on the location, size, potential risk, and other factors relating to a gas leak, repair crews will be better prepared to fix leak.
Finally, an increasing problem for utilities is the growing number of damages caused by no calls to 811. Advanced Technologies and Services uses predictive AI technology to identify areas where no calls to 811 call centers are more likely to provide a way to further reduce damages.
While it can be difficult to reach excavators that do not contact 811, algorithms can also harness targeted advertisements for 811 to encourage individuals or companies to notify 811 centers before embarking on their projects. If an individual still does not call 811, this AI can cross-reference data from local government and utility services to see if a building permit was granted, thereby giving insight into a particular no-call dig. No call digs from operators who know about 811 but begin a project without notifying due to expediency or exigent circumstances may be more inclined to call if AI can help speed up or prioritize tickets.
Artificial Intelligence has multiple applications for the damage prevention industry, however one commonality between them all is collecting accurate data to help determine predictability.
Whether AI is used for detecting or alerting for damages, workers stand to benefit from enhanced safety measures like timely data collected through machine learning algorithms. For methods of tracking damages like the DIRT Report, Artificial Intelligence could help revolutionize the way that both One-Call centers and national organizations track, report, and work to reduce damages in the future.
Written by Roy Mathews, Public Policy Associate
Interested in other technology highlights? Stay tuned for more ways technology is making damage prevention safer.
The Alliance for Innovation and Infrastructure (Aii) is an independent, national research and educational organization. An innovative think tank, Aii explores the intersection of economics, law, and public policy in the areas of climate, damage prevention, energy, infrastructure, innovation, technology, and transportation.