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

Applied Sciences

卷:9

Evangelos Manthos¹  Nicola Baldo²  Matteo Miani² 

[1] Department of Civil Engineering, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece;

[2] Polytechnic Department of Engineering and Architecture, University of Udine, Via del Cotonificio 114, 33100 Udine, Italy;

关键词: artificial neural networks;    hot mix asphalt;    diabase aggregates;    limestone aggregates;    polymer modified bitumen;    stiffness modulus;    Marshall test;    cross-validation;    model selection;   

DOI  :  10.3390/app9173502

来源: DOAJ

【 摘 要 】

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 ( $R$ ) and mean square errors; in particular, $R$ values were within the range $0.965-0.919$ in the training phase and $0.881-0.834$ in the CV testing phase, depending on the predicted parameters.

【 授权许可】

Unknown