A. S. M. Asifur Rahman

Development of a nonlinear rutting model for asphalt concrete based on Weibull parameters

Abstract In this study, a regression-based predictive model is developed from laboratory test data to determine rutting performance of Superpave asphalt-aggregate mixtures. A Hamburg Wheel Tracking Device was used to measure the rutting performances of the asphalt concrete samples. A total of eighteen (18) asphalt concrete mixtures were tested and used in this study. Laboratory rut data were fitted by Weibull distribution and the Weibull parameters were correlated to the mix properties, such as, aggregate gradation, binder property, effective specific gravity of the aggregate and mix volumetrics of the asphalt concrete samples. Statistical evaluation showed that a fairly accurate estimation of rut depth can be found by using the regression-based predictive model developed in this study.

Effect of Binder Performance Grade on the Dynamic Modulus Mastercurves of SP III Superpave Mixes in New Mexico

This study evaluates the effects of asphalt binders Performance Grade (PG) on the dynamic modulus (|E*|) mastercurves of Superpave (SP) mixes typically found in the state of New Mexico. A total of three Hot-Mix Asphalt (HMA) mixes were collected. They were produced from three different binder grades and one with aggregate gradation containing 35% of Reclaimed Asphalt Pavement (RAP). Field collected HMA mixes were compacted and cored to cylindrical specimens. The specimens were then tested for dynamic modulus at five different temperatures and six different frequencies of loading. Mastercurves for all the HMA mixes are then developed and compared. A noticeable amount of divergence was found while comparing these mastercurves among each other. It is observed that stiffer binder produces HMA concrete with high dynamic modulus value. However, this trend is not always true. In case of HMA mixes with RAP, the dynamic modulus of the HMA increases considerably, though the mix has a softer binder. The dynamic modulus of these RAP-included HMA materials could be as high as twice the dynamic modulus of HMA without RAP.

Dynamic modulus and phase angle models for New Mexico’s Superpave mixtures

This study modifies the viscosity (η)-based Witczak dynamic modulus (|E*|) predictive equation which is used in the recently developed AASHTOWare pavement Mechanistic-Empirical design software. A total of 21 Superpave asphalt concrete mixtures with different gradations and binder grades were tested for |E*| at different temperatures and frequencies of loading. Results show that the η-based Witczak model underestimates the |E*| for New Mexico’s Superpave mixes. Therefore, the revised model is developed using regression analysis. A phase angle (φ) predictive model is also developed using the φ-values obtained during the |E*| testing. It was found that the revised η-based Witczak model determines the |E*| more accurately compared to the original η-based Witczak model.

Viscosity-Based Complex Modulus and Phase-Angle Predictive Models for the Superpave Asphalt Mixtures of New Mexico

AbstractThe recently developed mechanistic-empirical design method of pavement uses the nationally calibrated dynamic modulus predictive model for the design and analysis of asphalt pavements. This...

Metal reactivity in laboratory burned wood from a watershed affected by wildfires

We investigated interfacial processes affecting metal mobility by wood ash under laboratory-controlled conditions using aqueous chemistry, microscopy, and spectroscopy. The Valles Caldera National Preserve in New Mexico experiences catastrophic wildfires of devastating effects. Wood samples of Ponderosa Pine, Colorado Blue Spruce, and Quaking Aspen collected from this site were exposed to temperatures of 60, 350, and 550 °C. The 350 °C Pine ash had the highest content of Cu (4997 ± 262 mg kg-1), Cr (543 ± 124 mg kg-1), and labile dissolved organic carbon (DOC, 11.3 ± 0.28 mg L-1). Sorption experiments were conducted by reacting 350 °C Pine, Spruce, and Aspen ashes separately with 10 μM Cu(II) and Cr(VI) solutions. Up to a 94% decrease in Cu(II) concentration was observed in solution while Cr(VI) concentration showed a limited decrease (up to 13%) after 180 min of reaction. X-ray photoelectron spectroscopy (XPS) analyses detected increased association of Cu(II) on the near surface region of the reacted 350 °C Pine ash from the sorption experiments compared to the unreacted ash. The results suggest that dissolution and sorption processes should be considered to better understand the potential effects of metals transported by wood ash on water quality that have important implications for postfire recovery and response strategies.

Effect of Fineness Modulus and Uniformity Coefficient on the Complex Modulus Function of Asphalt Concrete

Different material attributes such as mix volumetrics, aggregate gradations, and binder characteristics are the factors affecting viscoelastic material functions of asphalt concrete. In this study, the effects of aggregate gradation on the complex modulus function of asphalt concrete are determined. The two distinct properties of the aggregate blend considered in this study are the fineness modulus and the uniformity coefficient. A total of 54, plant produced, asphalt concrete mixtures with asphalt binders having various performance grades and sources were collected from the manufacturing plants. The asphalt-aggregate mixtures were then compacted, cored, and sawed to cylindrical specimens. Three cylindrical specimens from each of the asphalt-aggregate mixtures were prepared and tested in the laboratory for complex or dynamic modulus. After that, average mastercurves of complex modulus and phase angle were generated by applying time-temperature superposition principle. Study showed that the complex modulus function of asphalt concrete is significantly related to the fineness modulus and uniformity coefficient of the aggregate blends used in the asphalt-aggregate mixture.Copyright

Predicting Dynamic Modulus of Asphalt Concrete From Binder Rheological Properties

This study proposes a completely new regression-based predictive model to estimate dynamic modulus (|E*|) of asphalt concrete (AC) from the dynamic shear modulus (|Gb*|) and binder phase angle (δb) of the asphalt binder used in the AC mix. Other parameters related to the aggregate gradation and volumetrics are also incorporated in the model. In this study, a total of ten AC mixes with four binders having different Performance Grades (PG) and sources were collected from the manufacturing plants. The AC mixes were compacted and cored to cylindrical specimens. After that, the samples were tested in the laboratory for |E*| and AC phase angle (ϕ) at different temperatures and loading frequencies. The collected binders were tested for |Gb*| and δb using dynamic shear rheometer (DSR). The statistical assessment showed that a fairly accurate estimation of |E*| and ϕ can be obtained by using these new predictive models.

Comparing laboratory dynamic modulus values with long term pavement performance predictions

AbstractThis study compares laboratory dynamic modulus value of Superpave mixes with the dynamic modulus obtained from Long Term Pavement Performance (LTPP) database. The comparison shows that the dynamic modulus from LTPP database, which were determined by using different types of artificial neural network (ANN) models, differs from the laboratory tested dynamic modulus. The dynamic modulus data of five LTPP test sections are considered. Mixes similar to those five sections were collected from the field and tested in the laboratory. Based on the findings of this study, it can be said that dynamic modulus from ANN models are less than the laboratory dynamic modulus for New Mexico Superpave mixes. Therefore, as an important design parameter, the use of dynamic modulus predicted from Neural Network models can result in outcomes different from those using laboratory dynamic modulus.