Interpretation of Indirect Tension Test Based on Viscoelasticity

Drescher, A.; Newcomb, D E; Zhang, Wenfen

doi:10.3141/1590-06

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…Therefore, determination of ΈE*Έ from the IDT test requires a more complex formulation than uniaxial testing (e.g., AASHTO TP62-03) requires. Drescher et al (7) and Zhang et al (8) applied the theory of viscoelasticity to linear elastic solutions developed by Hondros (9) to develop linear viscoelastic solutions for stresses, strains, complex compliances, phase angle, and Poisson's ratio from the IDT test. The three-dimensional analysis includes calculations for deviatoric or shear stresses and volumetric stresses and strains (7 ).…”

Section: Determination Of Dynamic Modulus Using Idt Testmentioning

confidence: 99%

“…Drescher et al (7) and Zhang et al (8) applied the theory of viscoelasticity to linear elastic solutions developed by Hondros (9) to develop linear viscoelastic solutions for stresses, strains, complex compliances, phase angle, and Poisson's ratio from the IDT test. The three-dimensional analysis includes calculations for deviatoric or shear stresses and volumetric stresses and strains (7 ). To validate the solutions, the creep compliance from a creep test was used to calculate the horizontal and vertical displacements from a pulse loading test, that is, the M R test.…”

Section: Determination Of Dynamic Modulus Using Idt Testmentioning

confidence: 99%

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Predicting the Resilient Modulus of Asphalt Concrete from the Dynamic Modulus

Lacroix

Khandan

Kim

2007

Transportation Research Record: Journal of the Transportation R

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The NCHRP 1-37A Guide for Mechanistic-Empirical Design of New and Rehabilitated Design Structures introduces the dynamic modulus as the material property to characterize asphalt concrete. This is a significant change from the resilient modulus used in the previous AASHTO pavement design guide. This paper presents an analytical method of calculating the resilient modulus from the dynamic modulus. It involves the application of multiaxial linear viscoelastic theory to linear elastic solutions for the indirect tension test developed by Hondros. The prediction method is verified by using three 12.5-mm surface course mixtures with different aggregate shapes and binder types and one 25.0-mm base mixture. Results show that the predicted and measured resilient modulus values are in close agreement. The results provide a forward model for the potential back-calculation of the dynamic modulus from resilient modulus databases already available in highway agencies, such as the Long-Term Pavement Performance Materials Database.

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Section: Determination Of Dynamic Modulus Using Idt Testmentioning

confidence: 99%

Section: Determination Of Dynamic Modulus Using Idt Testmentioning

confidence: 99%

Predicting the Resilient Modulus of Asphalt Concrete from the Dynamic Modulus

Lacroix

Khandan

Kim

2007

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

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Revisiting the relationship of dynamic and resilient modulus test for asphaltic concrete mixtures

Ali

Irfan

Zeeshan

et al. 2018

Construction and Building Materials

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Looking to the future: the next-generation hot mix asphalt dynamic modulus prediction models

Ceylan

Gopalakrishnan

Kim

2009

International Journal of Pavement Engineering

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This paper describes an innovative approach related to the development of a new hot mix asphalt (HMA) dynamic modulus (|E*|) prediction model by employing the artificial neural networks (ANNs) methodology. Many studies have been conducted over the last 50 years related to the development of HMA |E*| prediction models based on the regression analysis of laboratory measurements. The current study is an attempt to replace the regression analysis with the ANNs that have proved useful for solving certain types of problems that are too complex, poorly understood or resource intensive to tackle using more traditional numerical and statistical methods. The ANN |E*| prediction models were developed using the latest comprehensive |E*| database that is available to the researchers (from the NCHRP Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the 1999 version of the Witczak |E*| prediction model, which is included in the mechanistic-empirical pavement design guide (MEPDG) and the new revised version as well. The sensitivity of input variables to the ANN model predictions were also examined and discussed. The ANN |E*| models show significantly higher prediction accuracy compared with the existing regression models and could easily be incorporated into the MEPDG. This approach may lead to more accurate characterisation of the HMA dynamic modulus resulting in better performance prediction, thereby reducing the risk of premature pavement failure. Reference to this paper should be made as follows: Ceylan, H., Gopalakrishnan, K., and Reference to this paper should be made as follows: Ceylan, H., AbstractThis paper describes an innovative approach related to the development of a new Hot-Mix Asphalt (HMA) dynamic modulus (E*) prediction model by employing the Artificial Neural Networks (ANN) methodology. Many studies have been conducted over the last 50 years related to the development of HMA E* prediction models based on regression analysis of laboratory measurements. The current study is an attempt to replace the regression analysis with ANNs which have proved useful for solving certain types of problems too complex, too poorly understood, or too resource-intensive to tackle using more-traditional numerical and statistical methods. The ANN E* prediction models were developed using the latest comprehensive E* database that is available to the researchers (from the NCHRP Report 547) containing 7,400 data points from 346 HMA mixtures. The ANN model predictions were compared with the 1999 version of the Witczak E* prediction model which is included in the Mechanistic-Empirical Pavement Design Guide (MEPDG) and the new revised version as well. The sensitivity of input variables to ANN model predictions were also examined and discussed. The ANN E* models show significantly higher prediction accuracy compared to the existing regression models and could easily be incorporated in the MEPDG. This approach may lead to more accurate characterization of HMA d...

show abstract

Interpretation of Indirect Tension Test Based on Viscoelasticity

Cited by 5 publications

References 1 publication

Predicting the Resilient Modulus of Asphalt Concrete from the Dynamic Modulus

Predicting the Resilient Modulus of Asphalt Concrete from the Dynamic Modulus

Revisiting the relationship of dynamic and resilient modulus test for asphaltic concrete mixtures

Looking to the future: the next-generation hot mix asphalt dynamic modulus prediction models

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