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Improved models for the prediction of asphalt binder dynamic shear modulus and phase angle
https://orcid.org/0000-0003-2849-5263
https://orcid.org/0000-0003-0889-6078
Highlights New models for binder dynamic shear modulus and phase angle from viscosity. Based on generalized logistic functions with physical model parameters. Total of 7,120 binder data points considered in model development. Very accurate predictions compared to existing predictive models. “Reduced viscosity” with single value shift function identified.
Abstract In this paper, new set of models for predicting the dynamic shear modulus and phase angle of asphalt binders from conventional viscosity are developed and presented. Based on theoretical considerations, suitable forms of general logistic functions are introduced to develop new models for binder dynamic shear modulus and phase angle over a wide range of temperature and loading conditions. A nonlinear optimization technique based on the Nelder-Mead optimization method is used to obtain the coefficients for the new binder dynamic shear modulus and phase angle models. The experimental binder testing database consisting of dynamic shear modulus, phase angle, frequency, viscosity measurements of thirty-two (32) unmodified and nine (9) modified binders from different sources is used in the study. A total of 7, 120 binder data points are used for the model development, evaluation and validation. The new models deliver very accurate prediction of the binder dynamic shear modulus and phase angle from the conventional steady-state viscosity indicated by very high goodness of fit using 7,120 binder data points. A new parameter termed the “reduced viscosity” is identified which produces a smooth continuous function of the binder phase angle for both modified and unmodified binders using a single viscosity shift factor. The reduced viscosity is considered as the independent variable in the new dynamic shear modulus and phase angle predictive models.
Improved models for the prediction of asphalt binder dynamic shear modulus and phase angle
https://orcid.org/0000-0003-2849-5263
https://orcid.org/0000-0003-0889-6078
Highlights New models for binder dynamic shear modulus and phase angle from viscosity. Based on generalized logistic functions with physical model parameters. Total of 7,120 binder data points considered in model development. Very accurate predictions compared to existing predictive models. “Reduced viscosity” with single value shift function identified.
Abstract In this paper, new set of models for predicting the dynamic shear modulus and phase angle of asphalt binders from conventional viscosity are developed and presented. Based on theoretical considerations, suitable forms of general logistic functions are introduced to develop new models for binder dynamic shear modulus and phase angle over a wide range of temperature and loading conditions. A nonlinear optimization technique based on the Nelder-Mead optimization method is used to obtain the coefficients for the new binder dynamic shear modulus and phase angle models. The experimental binder testing database consisting of dynamic shear modulus, phase angle, frequency, viscosity measurements of thirty-two (32) unmodified and nine (9) modified binders from different sources is used in the study. A total of 7, 120 binder data points are used for the model development, evaluation and validation. The new models deliver very accurate prediction of the binder dynamic shear modulus and phase angle from the conventional steady-state viscosity indicated by very high goodness of fit using 7,120 binder data points. A new parameter termed the “reduced viscosity” is identified which produces a smooth continuous function of the binder phase angle for both modified and unmodified binders using a single viscosity shift factor. The reduced viscosity is considered as the independent variable in the new dynamic shear modulus and phase angle predictive models.
Improved models for the prediction of asphalt binder dynamic shear modulus and phase angle
https://orcid.org/0000-0003-2849-5263
https://orcid.org/0000-0003-0889-6078
Highlights New models for binder dynamic shear modulus and phase angle from viscosity. Based on generalized logistic functions with physical model parameters. Total of 7,120 binder data points considered in model development. Very accurate predictions compared to existing predictive models. “Reduced viscosity” with single value shift function identified.
Abstract In this paper, new set of models for predicting the dynamic shear modulus and phase angle of asphalt binders from conventional viscosity are developed and presented. Based on theoretical considerations, suitable forms of general logistic functions are introduced to develop new models for binder dynamic shear modulus and phase angle over a wide range of temperature and loading conditions. A nonlinear optimization technique based on the Nelder-Mead optimization method is used to obtain the coefficients for the new binder dynamic shear modulus and phase angle models. The experimental binder testing database consisting of dynamic shear modulus, phase angle, frequency, viscosity measurements of thirty-two (32) unmodified and nine (9) modified binders from different sources is used in the study. A total of 7, 120 binder data points are used for the model development, evaluation and validation. The new models deliver very accurate prediction of the binder dynamic shear modulus and phase angle from the conventional steady-state viscosity indicated by very high goodness of fit using 7,120 binder data points. A new parameter termed the “reduced viscosity” is identified which produces a smooth continuous function of the binder phase angle for both modified and unmodified binders using a single viscosity shift factor. The reduced viscosity is considered as the independent variable in the new dynamic shear modulus and phase angle predictive models.
Improved models for the prediction of asphalt binder dynamic shear modulus and phase angle
Onifade, Ibrahim (author) / Birgisson, Bjorn (author)
2020-03-13
Article (Journal)
Electronic Resource
English