Asphalt Mixture Quality Acceptance using the Hamburg Wheel-Tracking Test

Batioja-Alvarez, Dario; Lee, Jusang; Rahbar-Rastegar, Reyhaneh; Haddock, John E.

doi:10.1177/0361198120919749

Cited by 9 publications

(3 citation statements)

References 9 publications

(30 reference statements)

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“…Accordingly, better rutting performance for coarser gradations was reported by Larrian and Tarefder [11]. Batioja et al [12] studied the potential of Hamburg as a quality assurance test to be implemented in the state of Indiana. To this end, the effects of binder grade, NMAS, and testing temperature on both field cores and plant produced lab compacted samples were investigated through the rutting resistance index (RRI).…”

Section: Introductionmentioning

confidence: 93%

“…It was observed that in all four mixtures, the field cores accumulated lower rut depth as compared to the gyratory compacted samples in the lab. However, results reported in [12] showed better rutting resistance in plant-produced, laboratory-compacted (PPLC) specimens. The significant effect of sample type such as field core, PPLC and labproduced, lab-compacted (LPLC) has been reported in other studies [25].…”

Section: Introductionmentioning

confidence: 97%

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A deep learning approach to predict Hamburg rutting curve

Majidifard

Jahangiri

Rath

et al. 2021

Road Materials and Pavement Design

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Rutting continues to be one of the principal distresses in asphalt pavements worldwide. This type of distress is caused by permanent deformation and shear failure of the asphalt mix under the repetition of heavy loads. The Hamburg wheel tracking test (HWTT) is a widely used testing procedure designed to accelerate, and to simulate the rutting phenomena in the laboratory. Rut depth, as one of the outputs of the HWTT, is dependent on a number of parameters related to mix design and testing conditions. This study introduces a new model for predicting the rutting depth of asphalt mixtures using a deep learning technique -the convolution neural network (CNN). A database containing a comprehensive collection of HWTT results was used to develop a CNNbased machine learning prediction model. The database includes 10,000 rutting depth data points measured across a large variety of asphalt mixtures. The model has been formulated in terms of known influencing mixture variables such as asphalt binder high temperature performance grade, mixture type, aggregate size, aggregate gradation, asphalt content, total asphalt binder recycling content, and testing parameters, including testing temperature and number of wheel passes. A rigorous validation process was used to assess the accuracy of the model to predict total rut depth and the HWTT rutting curve. A sensitivity analysis is presented, which evaluates the effect of the investigated variables on rutting depth predictions by the CNN model. The model can be used as a tool to estimate the rut depth in asphalt mixtures when laboratory testing is not feasible, or for cost saving, pre-design trials.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 97%

A deep learning approach to predict Hamburg rutting curve

Majidifard

Jahangiri

Rath

et al. 2021

Road Materials and Pavement Design

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“…Several research studies have been conducted to identify the key factors that significantly affect the rutting and stripping potential of AC materials. Aggregate type and gradation, performance grade binder (PG), reclaimed asphalt pavement (RAP) content, antistripping agents, air voids content, temperature, and so forth, play a vital role in the rutting performance of AC materials (9)(10)(11)(12)(13)(14)(15)(16)(17). A study by Tarefder and Zamman revealed that air void content and aggregate gradation have a significant effect on pavement rutting performance (10).…”

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confidence: 99%

New Model for Predicting Rutting Performance of Superpave Asphalt Mixes

Hasan

Tarefder

2022

Transportation Research Record

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This study presents a new model for predicting rutting performance and stripping characteristics of Superpave asphalt concrete (AC) mixes from laboratory Hamburg Wheel Tracking Device (HWTD) test data. A total of 33 AC mixes were used and tested for HWTD to determine the rutting and stripping susceptibility of the mixes. The combined Franken–power approach was used to fit the rut data. The pertinent regression coefficients were then correlated to the mix’s physical and volumetric properties, that is, binder grade, reclaimed asphalt pavement content, effective asphalt content, voids in mineral aggregate, and aggregate gradation. A logistic regression analysis was employed in the model to capture the stripping characteristic of the mixes. The developed model was validated with a new set of HWTD test data. The model was observed to be capable of predicting the rutting and stripping characteristics of the AC mixes, which agreed reasonably well with laboratory test results. Therefore, the proposed model could be used for mix screening purposes at the mix design stage to avoid the significant consequences of failure in the field, and as a tool to determine rutting performance when laboratory testing is not feasible.

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Investigation on the mechanism and performance of asphalt and its mixture regenerated by waste engine oil

Chen

et al. 2021

Construction and Building Materials

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Asphalt Mixture Quality Acceptance using the Hamburg Wheel-Tracking Test

Cited by 9 publications

References 9 publications

A deep learning approach to predict Hamburg rutting curve

A deep learning approach to predict Hamburg rutting curve

New Model for Predicting Rutting Performance of Superpave Asphalt Mixes

Investigation on the mechanism and performance of asphalt and its mixture regenerated by waste engine oil

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