A New Design Methodology of Asphalt Mixture Dynamic Modulus Based on Pavement Response

Area of Science:

• Civil Engineering
• Materials Science
• Computational Mechanics

Background:

• Traditional asphalt mixture design is empirical, leading to a disconnect with pavement structure design.
• A rational approach is needed to integrate asphalt material properties with structural performance.

Purpose of the Study:

• To bridge the gap between asphalt mixture modulus and pavement structural behavior.
• To develop a method for determining dynamic modulus master curve parameters based on pavement responses.

Main Methods:

• Constructed pavement models with varying base rigidities (cement concrete, cement-treated granular, granular).
• Utilized Response Surface Method (RSM) to analyze parameter influence on pavement responses.
• Developed a Whale Optimization Algorithm-Back Propagation (WOA-BP) artificial neural network for pavement response prediction.
• Created a large database for optimizing dynamic modulus master curve parameters.

Main Results:

• Pavement structure significantly influences the impact of dynamic modulus master curve parameters on critical responses.
• Parameter 'δ' showed the most significant impact, followed by 'α'.
• The WOA-BP model demonstrated high accuracy in predicting pavement responses.

Conclusions:

• Successfully implemented a methodology to determine dynamic modulus master curve parameters from critical pavement responses.
• The findings facilitate a more integrated and accurate asphalt pavement design process.
• Provides a foundation for enhanced material-structure correlation in asphalt pavement engineering.