Abstract

ABSTRACT Laboratory testing is widely used to characterise fatigue properties of asphalt mixtures for the design of asphalt pavements. The recently introduced Three-Point Bending Cylinder (3PBC) test has proven to be an advantageous laboratory testing alternative. However, the standard sample size for the 3PBC test may hamper its use for testing as-built thin lifts of asphalt pavement. In this study, the potential to alter the sample size for 3PBC test was investigated. Various numerical techniques were explored to relate the fatigue performance in terms of the number of cycles to failure (Nf) among varying specimen diameters. In addition to Nf of the tested 3PBC sample diameter Nf (Di), strain level; 3PBC test sample diameter Di; and dynamic modulus |E*|; were selected as input variables to correlate with the Nf for the reference geometry Nf(D.R.). Initially, multi-linear regression (MLR) analysis was applied to inspect if any cogent relation could be achieved among the Nf values for different 3PBC sample diameters. In addition, multigene genetic programming (MGGP) and artificial neural network (ANN) analyses were performed to check if a better fit could be achieved. Both these techniques, however, revealed similar statistical results to the MLR model, with the ANN model achieving higher statistics.

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