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

被引：40
作者：
Baldo, Nicola [1 ]
Manthos, Evangelos [2 ]
Miani, Matteo [1 ]
机构：
[1] Univ Udine, Polytech Dept Engn & Architecture, Via Cotonificio 114, I-33100 Udine, Italy
[2] Aristotle Univ Thessaloniki, Dept Civil Engn, Univ Campus, Thessaloniki 54124, Greece
来源：
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 17期
关键词：
artificial neural networks; hot mix asphalt; diabase aggregates; limestone aggregates; polymer modified bitumen; stiffness modulus; Marshall test; cross-validation; model selection; CONTINUUM DAMAGE MODEL; VISCOPLASTIC MODEL; CONSTITUTIVE MODEL; BITUMINOUS MIXTURES; FINITE-ELEMENT; CREEP-BEHAVIOR; CONCRETE; PREDICTION; PERFORMANCE;
D O I：
10.3390/app9173502
中图分类号：
O6 [化学];
学科分类号：
0703 ;
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.
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页数：24
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