Evaluation of ANN-Based Dynamic Modulus Models of Asphalt Mixtures: Fid-Bau Portal

Evaluation of ANN-Based Dynamic Modulus Models of Asphalt Mixtures

Barugahare, Javilla / Amirkhanian, Armen N. / Xiao, Feipeng / Amirkhanian, Serji N.

Artificial neural network (ANN)-based dynamic modulus $| E^{*} |$ models were evaluated on South Carolina’s asphalt mixtures, the majority of which contained recycled asphalt pavement (RAP). These ANNs contained similar input variables as the NCHRP 1-40D and Hirsch regression models and were implemented in the neural network toolbox of MATLAB version R2018b. Two previously published ANN-based $| E^{*} |$ models were also evaluated on the same database. Most ANNs in the literature have been shown to predict $| E^{*} |$ with good success; however, they have not been validated outside of their original studies. The results showed that (1) ANN-based $| E^{*} |$ models performed significantly better than regression models; (2) ANNs with few input variables (either $V_{a}$ , $V_{b e f f}$ , and $G_{b}^{*}$ or VMA, VFA, and $G_{b}^{*}$ ) highly predicted $| E^{*} |$ with $R^{2} > 0.99$ on testing; (3) ANNs can accurately predict $| E^{*} |$ of recycled asphalt mixtures; (4) the validation performance of the two published ANNs on South Carolina’s asphalt mixtures was ranked fair; and (5) locally customized ANNs are more accurate in the estimation of $| E^{*} |$ than globally calibrated ANNs or regression models.