Which Models Are Commonly Used to Predict Pavement Performance?

Pavement doesn’t fail overnight. Deterioration is a gradual process influenced by a combination of traffic, weather, materials, and maintenance history. The key to managing it effectively is prediction, understanding how pavement will age so that the right treatments can be applied at the right time.

That’s where pavement performance models come in.

These models use data and forecasting techniques to estimate future pavement conditions, helping cities and counties plan ahead, avoid costly surprises, and improve long-term outcomes for their road networks.

Why Predictive Modeling Matters

Accurate pavement forecasting leads to:

• Fewer surprises – unexpected failures are minimized

• Better budget planning – future needs can be anticipated

• Smarter timing – repairs can be timed before conditions worsen

  
  

This kind of proactive planning saves time and money — and helps extend the life of every mile of road.

Figure: Typical asphalt deterioration curve with corresponding treatment types and cost tiers. Early interventions help preserve condition and reduce lifecycle costs.

Common Types of Pavement Performance Models

1. Empirical Deterioration Curves

These models are based on observed historical data. They track how pavement condition (such as PCI) typically declines over time under specific conditions. Different curves are used depending on factors like road type, materials, and climate.

• Example: A local asphalt road may show a gradual PCI drop from 85 to 60 over 10 years without treatment.

  
  

2. Machine Learning and AI Models

Emerging technologies now allow agencies to process large, complex datasets to improve accuracy. AI-based models can identify patterns in pavement degradation, forecast future conditions, and support decision-making — all while working alongside human expertise, engineering standards, and quality control processes.

• Applications: PCI prediction, treatment timing, network-level prioritization

  
  

3. Treatment-Based Forecasting Models

These models incorporate maintenance and rehabilitation history to adjust future deterioration rates. For example, a road that received a surface seal may deteriorate more slowly than one with no recent treatments.

4. Condition-Based Decision Trees

Some systems use rule-based models (e.g., "if PCI < 60 and cracking is severe, then overlay within 2 years"). While simple, these models can help automate treatment recommendations and support consistent decision-making.

Key Factors That Influence Forecasting Models

• Traffic Volume & Load

• Climate and Weather Extremes

• Pavement Structure and Material Quality

• Past Treatments and Maintenance Intervals

• Surface Type (asphalt, concrete, composite)

  
  

Each factor contributes to how quickly pavement deteriorates — and how accurate the forecast will be.

In Summary

Pavement performance models are essential tools for maintaining and improving roadway networks. Whether based on empirical data, engineering principles, or machine learning, these models provide the insights needed to make informed decisions about maintenance timing, budgeting, and lifecycle planning.

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Want to learn how performance modeling can support smarter planning in your community?

Explore how data-driven pavement forecasting helps municipalities manage risk, reduce costs, and improve road outcomes. Contact us today.