Use of the Hamburg Wheel-Tracking Test to Characterize Asphalt Mixtures in Cool Weather Regions

Swiertz, Daniel; Cheng, Long; Teymourpour, Pouya; Bahia, Hussain U.

doi:10.3141/2633-03

Cited by 12 publications

(3 citation statements)

References 2 publications

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“…In colder climates such as Wisconsin, lower temperatures have been used to evaluate the rutting resistance of the mixtures. Swiertz et al [10] performed the Hamburg test at 45 and 50 ℃ and observed that the number of passes to reach 12.5 mm of rut depth at 50 ℃ could be as high as three times higher than that at 45 ℃. Considering four nominal maximum aggregate sizes (NMAS) including 25, 19, 12.5, 9.5, and 4.75 mm of Superpave mixtures, Swiertz et al [10] investigated the effect of aggregate gradation on the rut depth.…”

Section: Introductionmentioning

confidence: 99%

“…Swiertz et al [10] performed the Hamburg test at 45 and 50 ℃ and observed that the number of passes to reach 12.5 mm of rut depth at 50 ℃ could be as high as three times higher than that at 45 ℃. Considering four nominal maximum aggregate sizes (NMAS) including 25, 19, 12.5, 9.5, and 4.75 mm of Superpave mixtures, Swiertz et al [10] investigated the effect of aggregate gradation on the rut depth. The Hamburg testing results at 50 ℃ under 20,000 wheel passes showed that the rut depth slightly increased as the NMAS increased from 4.75 to 25 mm.…”

Section: Introductionmentioning

confidence: 99%

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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: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning approach to predict Hamburg rutting curve

Majidifard

Jahangiri

Rath

et al. 2021

Road Materials and Pavement Design

View full text Add to dashboard Cite

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“…To address this issue, Walubita et al ( 28 ) performed the Hamburg test at 50°C, 60°C, and 70°C. Swiertz et al ( 29 ) also investigated the appropriate testing temperature for the Hamburg wheel tracking test when used in a colder climate. In that study, conducted in Wisconsin, tests were carried out at 40°C, 45°C, and 50°C.…”

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confidence: 99%

Development of a Performance-Related Framework for Asphalt Mixture Design for the Illinois Tollway

Jahangiri

Rath

Majidifard

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Various agencies have begun to research and introduce performance-related specifications (PRS) for the design of modern asphalt paving mixtures. The focus of most recent studies has been directed toward simplified cracking test development and evaluation. In some cases, development and validation of PRS has been performed, building on these new tests, often by comparison of test values to accelerated pavement test studies and/or to limited field data. This study describes the findings of a comprehensive research project conducted at Illinois Tollway, leading to a PRS for the design of mainline and shoulder asphalt mixtures. A novel approach was developed, involving the systematic establishment of specification requirements based on: 1) selection of baseline values based on minimally acceptable field performance thresholds; 2) elevation of thresholds to account for differences between short-term lab aging and expected long-term field aging; 3) further elevation of thresholds to account for variability in lab testing, plus variability in the testing of field cores; and 4) final adjustment and rounding of thresholds based on a consensus process. After a thorough evaluation of different candidate cracking tests in the course of the project, the Disk-shaped Compact Tension—DC(T)—test was chosen to be retained in the Illinois Tollway PRS and to be presented in this study for the design of crack-resistant mixtures. The DC(T) test was selected because of its high degree of correlation with field results and its excellent repeatability. Tailored Hamburg rut depth and stripping inflection point thresholds were also established for mainline and shoulder mixes.

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Wheel-tracking performance of recycled concrete aggregate with recycled glass and brick in unbound pavements under elevated loads

Senanayake,

Arulrajah,

Maghool

et al. 2023

Smart Constr. Sustain. Cities

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As natural quarry materials become increasingly scarce and uneconomical, the construction industry has turned to sustainable alternatives such as construction and demolition (C&D) wastes and recycled glass for road construction. The aim of this study was to evaluate the performance of mixtures consisting of recycled glass (RG), crushed brick (CB) and recycled concrete aggregate (RCA) under varied traffic conditions. This assessment was conducted through wheel-tracking (WT) tests under simulated high-traffic conditions, which involved subjecting the mixtures to elevated vertical loads and an increased number of load cycles compared to previous studies. The study revealed that both RCA + 20%RG and RCA + 20%CB blends displayed comparable or slightly greater mean surface deformations than natural crushed rock under default conditions. The default conditions specified by the local road authority include an 8 kN wheel load and 40,000 loading cycles. The study further revealed that both blends displayed a consistent increase in rut depth as the number of cycles increased up to 100,000 while being subjected to a 20 kN wheel load. The maximum rut depth of RCA + 20%RG was close to the lower end of the maximum allowable rut depth range specified by road authorities. This suggests that these blends are at the limits of carrying heavier loads on highly trafficked roads.

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Use of the Hamburg Wheel-Tracking Test to Characterize Asphalt Mixtures in Cool Weather Regions

Cited by 12 publications

References 2 publications

A deep learning approach to predict Hamburg rutting curve

A deep learning approach to predict Hamburg rutting curve

Development of a Performance-Related Framework for Asphalt Mixture Design for the Illinois Tollway

Wheel-tracking performance of recycled concrete aggregate with recycled glass and brick in unbound pavements under elevated loads

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