Calibration of Inverted Asphalt Pavement Rut Prediction Model, Based on Full-Scale Accelerated Pavement Testing

Area of Science:

• Civil Engineering
• Materials Science
• Transportation Engineering

Background:

• Accurate prediction of rut depth (RD) in inverted asphalt pavements (IAPs) is crucial for effective design and maintenance.
• Existing models often lack precision due to complexities in material behavior and loading conditions.

Purpose of the Study:

• To establish and calibrate a rut depth prediction model for IAPs using full-scale accelerated pavement testing (APT).
• To develop a reliable method for predicting pavement performance and informing maintenance strategies.

Main Methods:

• Utilized a power function for rut progression modeling and divided APT loading history into units for cumulative RD analysis.
• Applied the nonlinear incremental recursive (IR) principle to account for material nonlinearity and loading history.
• Established a rut shift function to integrate laboratory and full-scale APT data for model calibration.

Main Results:

• Developed a mechanistic-empirical RD prediction model calibrated using APT data and the IR principle.
• Achieved low prediction errors, ranging from -2.16 mm to 1.18 mm, in a case study of IAP RD prediction.
• Demonstrated the model's adaptability for IAPs with different design schemes by updating material coefficients and finite element models.

Conclusions:

• The proposed APT-based calibration method provides accurate and reliable rut depth predictions for IAPs.
• This approach serves as a valuable reference for establishing flexible pavement performance prediction models.
• The calibrated model is essential for informed design and maintenance decisions in pavement engineering.