Utility directors, asset managers, and consulting engineers who need to make capital planning decisions that are defensible, data-driven, and rooted in real-world impact.

While Likelihood of Failure (LOF) predicts where breaks are likely to occur, and Cost of Failure (COF) estimates what each break will cost, Total Risk (TR) unites both perspectives — showing which failures matter most when probability and consequence intersect.

TR is built for those who must justify funding priorities to boards, regulators, and ratepayers, balancing technical risk with service reliability and public trust.

Total Risk (TR) is Fracta’s most comprehensive risk model — a blended scoring system that integrates:

Likelihood of Failure (LOF) – the statistical probability that a pipe will fail.

Cost of Failure (COF) – the modeled financial and social consequence of that failure.

Criticality and Context – proximity to vital infrastructure, dense populations, and high-visibility areas such as hospitals, schools, and business districts.

The result is a single, holistic Total Risk Score for every pipe segment — quantifying not just if a pipe will fail, but what happens when it does.

This combined view helps utilities clearly see which failures would be most disruptive both technically and socially, enabling smarter, defensible prioritization.

Make Risk Visible

Total Risk transforms complex data into intuitive, map-based visualizations that reveal where your system is most vulnerable. High-risk segments stand out immediately, helping engineers and planners direct resources to the areas that truly matter.

Support Defensible Capital Planning

TR provides a transparent foundation for investment decisions. Because it integrates both LOF and COF, every recommendation can be justified — not by age or intuition, but by data-backed risk.
When funding bodies or boards ask “Why this project?”, utilities can answer with clarity and confidence.

Protect Public Trust

Beyond dollars and data, TR measures what’s harder to quantify — public impact. It accounts for service disruption, traffic interference, and the reputational cost of visible failures near schools, hospitals, or city centers.
By prioritizing the pipes that protect people as well as infrastructure, utilities strengthen confidence in the systems that serve their communities.

Fracta’s Total Risk model builds upon the outputs of LOF and COF using advanced machine learning techniques and multi-variable weighting.

Integrating LOF and COF
Each pipe segment receives a probability of failure from the LOF model and a cost of failure from the COF model. These values are multiplied and normalized to generate an initial Total Risk value:

TR = LOF x COF

This forms the mathematical foundation of the risk calculation.

Adding Critical Context
Fracta’s AI enriches the model with spatial and social context — incorporating distance to critical facilities, service population density, road hierarchy, and land use intensity. These contextual features expand Total Risk beyond engineering metrics to include societal and operational impact.

Machine Learning Optimization
The TR model is trained on large, diverse datasets from utilities nationwide. Using ensemble learning and gradient boosting, the model identifies non-linear interactions between probability, consequence, and geography — fine-tuning weightings to reflect how different combinations of conditions translate into real-world disruption.

Continuous Calibration
As utilities collect new failure data, Fracta’s system automatically retrains the TR model, improving accuracy and ensuring the risk surface evolves with the system itself. Over time, this produces a “living” model — one that grows smarter, more localized, and increasingly predictive.