Pavement Express Performance Models

For more detailed information, see the following topics:

• PE Performance Model Setup
• PE Default Performance Model Structure
  • Add a Node to a Performance Model in PE
  • Delete a Node in a Performance Model in PE
  • Set Decision Parameters for a Performance Model in PE
• PE Performance Model Expressions
• PE Performance Model Assignment
  • PE Analyze Model
    • PE Analyze Models Window
    • PE Model Equations and Regression Methods
    • Select a Data Set for Analysis in PE
    • Perform Linear Regression in PE
    • Perform Non-linear Regression in PE
    • Perform Regression with Outliers Set by Standard Deviation Criteria in PE
    • Perform Regression with Outliers Set by User-defined Criteria in PE
    • Use a User-defined Model in PE
    • Assign a New Model to the Node in PE
• PE Performance Model Benefit
• Remaining Service Life (RSL) in PE

Pavement performance modeling is essential for good pavement management practices on all levels, from the project level to the network level. Pavement performance models are generally developed based on historical pavement data combined with engineering judgment. Pavement performance models can be broadly divided into two categories: group models and pavement management section models. Group models are models developed for a group of sections based on a set of categorical criteria, such as Pavement Type, Traffic Level, Client Region, etc. On the other hand, pavement management section models are models that are specified for each pavement management section based on its characteristic features. 

AgileAssets Pavement Express uses group-based models where a group is a set of pavement segments defined by one or more variables. These variables are called Performance Class Variables and are explained in detail in the sections listed above. Each group is then assigned a performance model for each performance index.

Pavement performance prediction is possibly the least technologically precise component of pavement management for the following reasons:

• There are uncertainties in a pavement's behavior under changeable traffic loading, environment, etc.
• It is difficult to quantify the numerous factors affecting pavement performance
• There is error associated with using discrete testing points to represent the total pavement area when estimating pavement condition
• The subjective nature of pavement condition surveys introduces errors in the data

To develop the best possible models from the available data and update these models as more data becomes available is a very important task for engineers and researchers in pavement management.

The system produces a wide range of deterministic models for groups of similar pavements. The software itself is highly flexible in terms of allowing these models to be developed.