Date of Award
8-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Civil Engineering
Committee Chair/Advisor
Dr. Bradley J Putman & Dr. Fabricio Leiva
Committee Member
Dr. Prasad Rangagu
Committee Member
Dr Jennifer Ogle
Abstract
The objective of the study was to evaluate the sensitivity of various input variables on the flexible pavement design thickness of high-speed, high-traffic routes in South Carolina using the Mechanistic-Empirical Pavement Design Guide (MEPDG) by means of the AASHTOWare Pavement ME Design software using global calibration coefficients with a focus on bottom-up fatigue cracking. The variables considered in this investigation included two-way average annual daily truck traffic (AADTT), asphalt mix type, climate station, subgrade type and resilient modulus, and aggregate base thickness. The study includes comparative analysis using older methods like the AASHTO 1993 method and the South Carolina DOT Pavement Design Guidelines, which is primarily based on the AASHTO 1972 method. Additionally, the study discusses the local calibration of the bottom-up fatigue cracking model for South Carolina for medium-level traffic and using machine learning methods like Artificial Neural Network (ANN), Gradient Boosted Method (GBM), and Random Forest (RF) to enhance model prediction. The results of the sensitivity analysis indicated that the asphalt mix type did not have a significant impact on the results. However, one of the five climate stations evaluated resulted in significantly thicker pavements than the others. Both subgrade types, as well as resilient modulus, had a significant effect on the pavement thickness. Finally, pavements were more sensitive to total truck traffic changes at lower AADTT values and then became somewhat less sensitive when exposed to the highest levels of traffic. The findings from the sensitivity study were used to develop a preliminary asphalt thickness design catalog for interstate routes in South Carolina. The results from the local calibration model showed high errors for bottom-up fatigue cracking in all the trials, but machine learning algorithm was able to increase prediction accuracy.
Recommended Citation
Girish, Shilpa, "Sensitivity Analysis of Flexible Pavement for South Carolina Conditions" (2025). All Dissertations. 3963.
https://open.clemson.edu/all_dissertations/3963