Calibration of dynamic modulus predictive model

Georgouli, Konstantina; Loizos, Andreas; Plati, Christina

doi:10.1016/j.conbuildmat.2015.10.163

Cited by 39 publications

(13 citation statements)

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“…The results showed that the NCHRP 1-40D and Hirsh models produced undesirable bias at low and high E* values [20]. Recently, Georgouli et al [21] tested 15 asphalt base and wearing course mixtures (1350 E* measurements) from Greece to evaluate the accuracy of the NCHRP 1-37A, NCHRP 1-40D, and Hirsch models. For asphalt base mixtures the results showed that the NCHRP 1-37A model produced accurate results.…”

Section: Comparison Studies Of Witczak and Hirsh Modelsmentioning

confidence: 99%

Comparison of Witczak NCHRP 1-40D & Hirsh dynamic modulus models based on different binder characterization methods: a case study

et al. 2017

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Abstract. The Pavement ME Design method considers the hot mix asphalt (HMA) dynamic modulus (E*) as the main mechanistic property that affects pavement performance. For the HMA, E* can be determined directly by laboratory testing (level 1) or it can be estimated using predictive equations (levels 2 and 3). Pavement-ME Design introduced the NCHRP1-40D model as the latest model for predicting E* when levels 2 or 3 HMA inputs are used. This study focused on utilizing laboratory measured E* data to compare NCHRP1-40D model with Hirsh model. This comparison included the evaluation of the binder characterization level as per Pavement ME Design and its influence on the performance of these models. E*tests were conducted in the laboratory on 25 local mixes representing different road construction projects in the kingdom of Saudi Arabia. The main tests for the mix binders were dynamic Shear Rheometer (DSR) and Brookfield Rotational Viscometer (RV). Results showed that both models with level 3 binder data produced very similar accuracy. The highest accuracy and lowest bias for both models occurred with level 3 binder data. Finally, the accuracy of prediction and level of bias for both models were found to be a function of the binder input level.

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Section: Comparison Studies Of Witczak and Hirsh Modelsmentioning

confidence: 99%

Comparison of Witczak NCHRP 1-40D & Hirsh dynamic modulus models based on different binder characterization methods: a case study

et al. 2017

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“…The Witczak 1-37A, Bari & Witczak, NCHRP 1-40D, Hirsch and Georgouli et al [25] models, as described in Eqs. 1, 3, 10 and 14, respectively, were used for the estimation of E Ã of HMA mixture.…”

Section: Comprehensive Research Studymentioning

confidence: 99%

“…Georgouli et al [25] developed a prediction model based on the Witczak 1-37A model in order to accurately predict the E Ã of the under investigation mixtures. Various asphalt base mixtures typically used nationally were considered and a total of 1350 data points were used for the model development.…”

Section: Georgouli Et Al Modelmentioning

confidence: 99%

“…The performance of these models to local mixtures is not consistent and seems to vary with the HMA mixture type [7,[15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Due to large bias in the E Ã prediction of local mixtures resulting from the aforementioned models, Georgouli et al [25] developed new prediction models (based on the sigmoidal function form) for the E Ã estimation of nationally HMA mixtures used in highways of the Greek transportation network.…”

Section: Introductionmentioning

confidence: 99%

“…Due to large bias in the E Ã prediction of local mixtures resulting from the aforementioned models, Georgouli et al [25] developed new prediction models (based on the sigmoidal function form) for the E Ã estimation of nationally HMA mixtures used in highways of the Greek transportation network.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of dynamic modulus prediction models in fatigue cracking estimation

2016

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The dynamic modulus (E Ã ) of Hot Mix Asphalt (HMA) mixtures is a key input parameter in the Mechanistic-Empirical (M-E) pavement design and analysis processes for the prediction of fatigue and rutting damage. The determination of E Ã in the laboratory requires specialized equipment and is an overall time consuming procedure. With this in mind, various prediction models have been developed over the years for the estimation of the E Ã , based on the volumetric properties of the HMA and the binder properties. Flexible pavement design processes require, amongst others, an accurate prediction of the fatigue behavior of the asphalt mixtures. With regards to M-E pavement design, a fatigue model to predict the number of load repetitions to fatigue cracking as a function of the tensile strain and the E Ã of the asphalt mixture is considered. Taking the above into consideration the aim of the present research study is the comparative assessment of the most widely used E Ã prediction models and their impact on the predicted fatigue cracking in the context of M-E pavement design in comparison. Further, the impact of an E Ã prediction model developed through calibration process is also investigated. For this purpose, an asphalt mixture and a pavement structure often implemented in highways of the national transportation network, was selected and fatigue cracking was calculated utilizing both, predicted E Ã and laboratory determined values. Analysis showed that the large bias in the E Ã prediction models is compensated to a certain extend in the final output which is the fatigue cracking. Relevant results from the sensitivity analysis are presented in the paper.

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