contributor author | Javilla Barugahare | |
contributor author | Armen N. Amirkhanian | |
contributor author | Feipeng Xiao | |
contributor author | Serji N. Amirkhanian | |
date accessioned | 2022-01-31T23:36:20Z | |
date available | 2022-01-31T23:36:20Z | |
date issued | 6/1/2021 | |
identifier other | %28ASCE%29MT.1943-5533.0003721.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4270028 | |
description abstract | 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 Va, Vbeff, and Gb* or VMA, VFA, and Gb*) highly predicted |E*| with R2>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. | |
publisher | ASCE | |
title | Evaluation of ANN-Based Dynamic Modulus Models of Asphalt Mixtures | |
type | Journal Paper | |
journal volume | 33 | |
journal issue | 6 | |
journal title | Journal of Materials in Civil Engineering | |
identifier doi | 10.1061/(ASCE)MT.1943-5533.0003721 | |
journal fristpage | 04021099-1 | |
journal lastpage | 04021099-10 | |
page | 10 | |
tree | Journal of Materials in Civil Engineering:;2021:;Volume ( 033 ):;issue: 006 | |
contenttype | Fulltext | |