Use of the Modified Ramberg-Osgood Material Model to Predict Dynamic Modulus Master Curves of Asphalt Mixtures

Primusz, Péter; Tóth, Csaba

doi:10.3390/ma16020531

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…Nowadays the most commonly used models are the Witczak model [11,32], the Hirsch model [33,34] and models using artificial neural networks (ANN models) [35][36][37]. New predictive models are also constantly being developed [38][39][40]. A new predictive model for calculating the stiffness modulus for the four-point bending beam method (4PBB) was developed based on a form of the Witczak-El-Badawy model equation for the dynamic-modulus method (DM), commonly used in countries designing pavements according to MEPDG.…”

Section: Predictive Modelmentioning

confidence: 99%

Development and Analysis of High-Modulus Asphalt Concrete Predictive Model

Bartkowiak

Słowik

2023

Materials

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The main purpose of this paper is to present the development of a new predictive model intended for the calculation of stiffness modulus |E*| determined by a four-point bending beam test (4PBB or 4PB-PR). The model developed, called model A, was based on the Witczak model, which was developed for the dynamic-modulus (DM) method. Most of the asphalt mixtures used to develop the model were high-modulus asphalt concrete (HMAC). The most commonly used methods for determining the stiffness modulus |E*| of asphalt mixtures were also discussed. The paper presents the results of the study for 10 asphalt mixtures but 8 of them were used to develop the predictive model. In addition, the results of complex shear modulus G* tests on neat and modified bituminous binders carried out in a dynamic shear rheometer (DSR), necessary for the development of a predictive model, are presented. The tests carried out in the dynamic shear rheometer had significant measurement uncertainties. The results of the volumetric parameters of the asphalt mixtures are also reported. The developed model A has maximum absolute errors e = 1930 MPa (p = 95%) and maximum relative errors re = 50% (p = 95%). The distribution of the absolute errors of the model, after discarding outliers, has a normal distribution as in the development of other models of this type, which was confirmed by appropriate statistical tests. On the basis of the tests and calculations carried out, it was concluded that, in order to increase the precision of the predictive models, it is advisable to reduce the measurement uncertainty of the bitumen complex shear modulus G*. For the developed model A, the limiting values of the stiffness modulus |E*| are also shown, within which the determined stiffness modulus should fall.

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Section: Predictive Modelmentioning

confidence: 99%

Development and Analysis of High-Modulus Asphalt Concrete Predictive Model

Bartkowiak

Słowik

2023

Materials

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“…References [43][44][45] utilized the Polynomial shift factor equation to construct the master curve of the asphalt mixture. Study [46] compared three shift factor equations (i.e., Arrhenius, WLF, log-linear) in the SLS model to build the master curve.…”

Section: Introductionmentioning

confidence: 99%

Study on the Construction of Dynamic Modulus Master Curve of Polyurethane Mixture with Dense Gradation

Zhao

Gao²,

Cui³

et al. 2023

Coatings

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The PU mixture considered here is a new kind of pavement material with excellent road performance, which lacks study into its dynamic mechanical and viscoelastic properties. In this study, the dynamic modulus of the polyurethane (PU) mixture was fitted by using five master curve models, five shift factor equations, and four error minimization methods. According to test results, the log–log plot form was able to more effectively display the differences between master curves. The solver method, the sum of square error minimization (≤0.02), proved to be more appropriate and accurate with higher fitting parameter results. The line of equality statistic and Pearson linear correlation analysis results demonstrated that WLF and Kaelble equations were appropriate for five master curve models with trend line R2 values higher than 0.98. The GLS and SCM model with the WLF equation had the most accurate master curve fitting results. The dynamic modulus master curve shape of the PU mixture did not follow the traditional smooth “S” shape and did not show the ultimate dynamic modulus at extreme frequency. The viscoelasticity of the PU mixture is quite different from that of the asphalt mixture. This study recommended the most accurate error minimization method, the master curve model, and shift factor equations for characterizing the dynamic properties of the PU mixture.

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