How to Improve Damage Prevention and Safety with Artificial Intelligence

Irth’s offerings for assessing risk are built to accommodate every customer, no matter how much historical damage data you have.

Smart Score: The backbone of Irth Insights for Damage Prevention.

You don’t get just a number; you get an explanation for why that number is what it is.

This is where you can configure risk scores — if-this, then-this statements. Most clients use this for location-based scores. For instance, if you have a high-profile or high-consequence area, you’d configure a higher risk score for that type of ticket.

Customers use a configurable risk score or location-based risk score when there is not enough historical data yet for an AI model to capture the risk in those areas. For example, a new subdivision is being built and micro-trenching needs to be done.

You can have the system search for certain keywords on an 811 ticket, such as boring or blasting activities, which would cause the system to identify it as a higher-risk ticket. Alternatively, you can also reduce the risk score on a ticket by keywords. For example, you could set a rule that if your team is the excavator on the ticket, reduce the score by so many points. Anything on a ticket from facility location to distance to facilities can be configured to increase or decrease a risk score.

You can also use Smart Score with other data besides ticket details, including industry trends, locate audits, depth of cover, and more. Irth is the only product in the industry where you can look at external activities outside of excavations and have the risk of an excavation modified.

Damage Risk Analysis (DRA): Answers the question, “What damages me?”

This analysis is based on your tickets, your damages, and your locators. It learns over time and modifies scores in real-time.

Irth’s DRA model refines risk score calculations using your organization’s performance data.

The risk score still evaluates the same variables for correlation with risk, but scores are customized to provide the most insight into how your organization operates — allowing you to better inform decisions, refine performance, and develop targeted preventative actions.

We’ve found the three most impactful variables in DRA to be:

1. What excavators have caused the most damage?
2. What facilities have the most damage?
3. What locators have been the most at fault for damages?

Insights with AI for Damage Prevention: AI and machine learning enhance risk assessment

At Irth, we use AI to:

• Increase safety and save you money by empowering you to predict incidents before they occur
• Make predictions highly accessible and easy to obtain
• Explain predictions in a way that is easy to understand and act upon
• Provider interpretability and transparency

Two main factors provide the best AI results:

1. Irth’s market-leading data catalog produces marketing-leading predictions.
2. Irth’s damage prevention expertise combines with really great data scientists, analysts, and engineers.

Irth quantifies and predicts risk using proven methods also used by NASA and the U.S. Department of Defense (likelihood versus impact). Machine learning allows us to identify risk and quantify what information predicts an incident and how much riskier it is.

Our models are getting retrained every single day. This prevents drift, the systematic degradation of predictive models over time.

Irth Insights with AI is fast, easy to integrate, scalable, and effective. With a 2-second average response time, a ticket is received and scored in almost real-time, and then automatic notifications are submitted and follow-up activities are created based on a risk matrix.

Benefits of Leveraging Artificial Intelligence in Damage Prevention 

With AI, our tools can proactively identify risk with predictive models, to give our customers immediate visibility into high-consequence/high-profile facilities, help focus damage prevention resources using real data, and automate steps to notify and assign the correct individuals or risks.

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