Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predictive Models for Storage Modulus and Loss Modulus of Asphalt Mixtures
AbstractComplex modulus of an asphalt mixture constitutes two components: E′ representing the ability of the mixture to store energy (elastic behavior), and E′′ reflecting the capacity of the material to dissipate energy (viscous behavior). The main objective of this study was to develop predictive equations for the two components, E′ and E′′, to better quantify and assess the performance of conventional and modified mixtures alternate to standard laboratory testing. The dataset used in this effort encompassed 163 conventional dense graded asphalt concrete (DGAC), 13 asphalt-rubber asphalt concrete (ARAC) gap-graded, and 9 asphalt-rubber friction course (ARFC) open-graded mixes covering 5,550 data points. Aggregate gradation, binder, and volumetric property parameters were used as predictor variables. Squared-error optimization mathematical techniques were employed in developing predictive models. The statistical goodness of fit measures of E′ and E′′ predictive models were very good to excellent. Validation results of the predictive models reflected effectiveness in reproducing observed values with goodness-of-fit measures in the domain of fair to excellent. Sensitivity performance analyses were also carried out to demonstrate the performance of asphalt mixtures with respect to different material properties.
Predictive Models for Storage Modulus and Loss Modulus of Asphalt Mixtures
AbstractComplex modulus of an asphalt mixture constitutes two components: E′ representing the ability of the mixture to store energy (elastic behavior), and E′′ reflecting the capacity of the material to dissipate energy (viscous behavior). The main objective of this study was to develop predictive equations for the two components, E′ and E′′, to better quantify and assess the performance of conventional and modified mixtures alternate to standard laboratory testing. The dataset used in this effort encompassed 163 conventional dense graded asphalt concrete (DGAC), 13 asphalt-rubber asphalt concrete (ARAC) gap-graded, and 9 asphalt-rubber friction course (ARFC) open-graded mixes covering 5,550 data points. Aggregate gradation, binder, and volumetric property parameters were used as predictor variables. Squared-error optimization mathematical techniques were employed in developing predictive models. The statistical goodness of fit measures of E′ and E′′ predictive models were very good to excellent. Validation results of the predictive models reflected effectiveness in reproducing observed values with goodness-of-fit measures in the domain of fair to excellent. Sensitivity performance analyses were also carried out to demonstrate the performance of asphalt mixtures with respect to different material properties.
Predictive Models for Storage Modulus and Loss Modulus of Asphalt Mixtures
Chandrappa, Anush K (Autor:in) / Biligiri, Krishna P / Kaloush, Kamil E / Venudharan, Veena
2016
Aufsatz (Zeitschrift)
Englisch
BKL:
56.45
Baustoffkunde
Lokalklassifikation TIB:
535/6520/6525/xxxx
Predictive Models for Storage Modulus and Loss Modulus of Asphalt Mixtures
| British Library Online Contents | 2016
Predictive Models for Storage Modulus and Loss Modulus of Asphalt Mixtures
| Online Contents | 2016
Revised Predictive Model for Dynamic (Complex) Modulus of Asphalt Mixtures
| British Library Conference Proceedings | 1996
Revised Predictive Model for Dynamic (Complex) Modulus of Asphalt Mixtures
| British Library Online Contents | 1996