Abstract
This study investigates the effect of nanoclay (NC) additives on the mechanical performance of hot mix asphalt (HMA). NC was incorporated at 0%, 2%, 4%, and 6% by binder weight, and the mixtures were tested for Marshall properties, indirect tensile strength (ITS), compressive strength, and resilient modulus (MR) at 5°C, 25°C, and 40°C. Results revealed notable improvements: Marshall stability increased by 26.9% at 6% NC, while flow values decreased by 13.8% up to 4% NC. ITS and compressive strength increased by 24.7% and 27.4%, respectively. MR values improved by 66%, 81.6%, and 60% at 5°C, 25°C, and 40°C, respectively, for 6% NC. An Artificial Neural Network (ANN) model was developed using nanoclay content and temperature as inputs to predict MR. The model achieved high accuracy (R² = 0.9434, RMSE = 508.87 MPa), demonstrating its effectiveness in capturing the nonlinear response of nanoclay-modified mixtures and providing a reliable predictive tool.