Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

The present paper discusses the analysis and modeling of laboratory data regarding the mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ) were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear transfer function. The effectiveness of such an approach was verified in terms of the coefficients of correlation (<inline-formula> <math display="inline"> <semantics> <mi>R</mi> </semantics> </math> </inline-formula>) and mean square errors; in particular, <inline-formula> <math display="inline"> <semantics> <mi>R</mi> </semantics> </math> </inline-formula> values were within the range <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0.965</mn> <mo>&#8722;</mo> <mn>0.919</mn> </mrow> </semantics> </math> </inline-formula> in the training phase and <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0.881</mn> <mo>&#8722;</mo> <mn>0.834</mn> </mrow> </semantics> </math> </inline-formula> in the CV testing phase, depending on the predicted parameters.

Abstract licence: CC BY

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