Optimizing truck platoon spacing to minimize asphalt concrete permanent deformation

Despite truck platoons’ benefits, their reduced lateral wander and shorter headway characteristics are expected to accelerate flexible pavement damage, particularly rutting. Existing pavement design methods often rely on layered elastic theory and empirical transfer functions, which limit their ability to capture complexity associated with the pavement system and vehicular loading, especially platoon traffic. To address this challenge, firstly, a conventional Burger model, containing a nonlinear power-law dashpot, was introduced, which allows predicting asphalt concrete (AC) deformation behavior. An incremental formulation of the model was derived to obtain deformation under arbitrary loading conditions. The derivation began with a one-dimensional (1D) formulation representing uniaxial stress-strain behavior, which was then extended to a three-dimensional (3D) formulation using principles of linear elasticity. Key components, including stress increments and the Jacobian matrix, were derived for implementation as a user subroutine in Abaqus. The model’s accuracy was first verified using an analytical solution for a simple creep loading scenario. Model parameters were calibrated using results from AC flow number and dynamic modulus tests. Validation against experimental data demonstrated the model’s effectiveness in accurately predicting AC permanent deformation across a range of stress levels. The nonlinear Burger model was incorporated into a validated 3D robust finite element (FE) pavement model. The pavement model accurately accounts for tire loading complexities, material characteristics, temperature variation, and layer interface interactions, ensuring realistic pavement behavior simulation. To efficiently simulate rut progression, the Load Pass Approach (LPA) was adopted, enabling the application of cyclic loading while maintaining computational efficiency and accuracy. To verify the model, a total of 24 simulations were completed, incorporating different pavement sections, material properties, traveling speeds, load types, and temperatures to assess AC rutting. The predicted rut accumulation predicted using the power-law model and LPA showed trends that are logical and closely aligned with real-world observations. An enhanced response prediction framework was developed to evaluate rut progression in flexible pavements under truck platoon loading. The framework incorporated nonlinear AC rutting through per-cycle normalization and accounted for axle configuration and load sequence using equivalence analysis. A total of 72 pavement simulations were conducted to evaluate the impact of varying rest periods on permanent deformation. Results showed that increased wander and higher platoon penetration distributed loads more uniformly, reducing localized rutting. Rut progression was influenced by two competing mechanisms: limited strain recovery, dominant at short rest periods, and hardening-relaxation, more prominent at longer rest periods. Temperature and material properties played a key role in governing these mechanisms, with softer materials at higher temperatures favoring limited recovery and stiffer materials at moderate temperatures promoting hardening-relaxation. A rest period of 0.57 sec (i.e., 60-ft truck spacing) minimized rutting, representing a balance between both mechanisms. Finally, as an extension of the developed pavement model, structure-induced rolling resistance force (F_RR^str) was calculated using a deflection-based approach. As steady-state deflection evolved over loading cycles, F_RR^str was calculated for each cycle, revealing three distinct trends: constant, increasing, or decreasing. Comparisons with a linear viscoelastic model showed that the permanent deformation model consistently produced higher F_RR^str, approximately 125% and 50% at 104 and 130°F, respectively. Temperature had the most significant impact on F_RR^str, followed by material properties, while rest period and speed had minimal effects. The study demonstrated the importance of mechanistic approaches (e.g., accurate prediction of AC rut behavior) to mitigate potential rutting through optimization.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Ramakrishnan, Aravind

Contributors dc:contributor

• Al-Qadi, Imad L.
• Roesler, Jeffery R.
• Duarte, Armando
• Chabot, Armelle
• Jayme, Angeli

• Asphalt Concrete
• Rutting
• Truck Platoon
• Connected And Autonomous Vehicle
• Pavement Model
• Pavement Rut
• Pavement Damage
• Rolling Resistance
• Finite Element Modeling
• Burger Model
• Hardening Relaxation

Statement dc:rights

• Copyright 2025 Aravind Ramakrishnan

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/130188

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F130188