ANN-based dynamic modulus models of asphalt mixtures with similar input variables as Hirsch and Witczak models

ABSTRACT

Artificial neural networks (ANNs) and G_b*-based regression models were used for the prediction of the dynamic modulus (|E*|) of South Carolina’s hot mix asphalt mixtures (HMAs) the majority of which contained recycled asphalt pavement (RAP). Models’ training and testing were done using a database that contained 1656 |E*| values from 93 HMA mixtures. G_b*-based models included the Hirsch, revised Hirsch, Bari-Witczak, revised Bari-Witczak, Al-Khateeb 1, Al-Khateeb 2, NCHRP 1-40D, and the simplified global models. The results showed that G_b*-based regression models had a significant bias in prediction; Coupling VMA and G_b* had the most influence on |E*|; four-layer ANNs generally had a better performance than three-layer ANNs on using Hirsch model’s related inputs; ANN 3-15-15-1 and ANN 8-15-15-1 (developed with similar input variables as the Hirsch and Witczak regression models respectively) showed very high performance of R² > 0.994 on testing. Therefore, ANNs could be considered to capture the influence of the binders’ rheological properties, mixture’s volumetric properties, and RAP on |E*| of HMA mixtures far better than regression-based models.

KEYWORDS:

• HMA mixtures
• dynamic modulus (|E*|)
• ANNs
• machine learning
• regression models

Acknowledgements

The HMA |E*| data referenced in this paper were obtained from the FHWA-SC-18-04 report. The views, opinions, and recommendations of the authors do not represent official statements and/or recommendations from the South Carolina Department of Transportation.

Data availability statement

The data that support the findings of this study are available from the corresponding author, [BJ], upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).