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• Design and fabricate a novel jaw system for asphalt elastic modulus testing • Assess modulus data distribution using skewness, Shapiro–Wilk, and Mann–Whitney U • Develop a GP model for modulus prediction as a loading-curve fitting tool • Perform nonlinear FEA in SolidWorks to validate the jaw's mechanical performance Reliable pavement design requires accurate asphalt elastic modulus, but conventional testing devices suffer from jaw misalignment, uneven load transfer, and specimen slippage, yielding high variability and limiting repeatability. Existing methods are often costly, error-prone, or lack integration with improved hardware, leaving a gap in precise, low-dispersion testing for diverse mixes. This study introduces a novel jaw design for elastic modulus testing that enhances clamping stability, minimizes eccentric loading, and promotes uniform strain distribution across the specimen. A genetic programming (GP)-based predictive model was developed to estimate asphalt elastic modulus, while nonlinear finite element analysis (FEA) in SolidWorks Simulation validated compressive stress–strain response in two laboratories (M1 and M2). The designed jaw yielded mean moduli of 804.25 MPa (SD 90.94 MPa, CV 11.31%) in M1 and 703.75 MPa (SD 69.34 MPa, CV 9.86%) in M2. Both groups depart from normality (in Shapiro–Wilk with p < 0.01 ), so nonparametric inference was used. The Mann–Whitney test gives U = 12 , two-tailed p ≈ 0.0406 with a large rank-biserial effect size r r b = 0.625 . GP modelling resulted in very good correspondence in measured and predicted values, with R² of 96.25% (M1) and 96.86% (M2) and RMSE of 19.16 MPa and 23.01 MPa, respectively, confirming the robustness and generalizability of the model. FEA simulation indicated maximum von Mises stresses of ∼77 MPa, displacements up to 9.44 mm, and equivalent strains approaching 0.38. Findings showed that the hardware–software innovations enhance modulus testing reliability while providing pavement engineers with a practical, interpretable predictive tool to bridge laboratory precision and field decisions.