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    Evaluation of ANN-Based Dynamic Modulus Models of Asphalt Mixtures

    Source: Journal of Materials in Civil Engineering:;2021:;Volume ( 033 ):;issue: 006::page 04021099-1
    Author:
    Javilla Barugahare
    ,
    Armen N. Amirkhanian
    ,
    Feipeng Xiao
    ,
    Serji N. Amirkhanian
    DOI: 10.1061/(ASCE)MT.1943-5533.0003721
    Publisher: ASCE
    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.
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      Evaluation of ANN-Based Dynamic Modulus Models of Asphalt Mixtures

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4270028
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    contributor authorJavilla Barugahare
    contributor authorArmen N. Amirkhanian
    contributor authorFeipeng Xiao
    contributor authorSerji N. Amirkhanian
    date accessioned2022-01-31T23:36:20Z
    date available2022-01-31T23:36:20Z
    date issued6/1/2021
    identifier other%28ASCE%29MT.1943-5533.0003721.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4270028
    description abstractArtificial 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.
    publisherASCE
    titleEvaluation of ANN-Based Dynamic Modulus Models of Asphalt Mixtures
    typeJournal Paper
    journal volume33
    journal issue6
    journal titleJournal of Materials in Civil Engineering
    identifier doi10.1061/(ASCE)MT.1943-5533.0003721
    journal fristpage04021099-1
    journal lastpage04021099-10
    page10
    treeJournal of Materials in Civil Engineering:;2021:;Volume ( 033 ):;issue: 006
    contenttypeFulltext
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