The interaction between asphalt binder and aggregate is fundamental to ensure adequate performance of asphalt mixtures, mainly in the presence of water. The work of adhesion generated between both materials directly affects the resistance of asphalt mixture to moisture damage, because it measures the ease with which water can displace asphalt binder from the aggregate surface. The objective of this study was to characterize the bond strength between asphalt and several aggregate sources. A PG 64-22 neat binder was modified with several additives to determine the effect on adhesion: polymers, nanomaterials, and adhesion promoters. To measure the strength of adhesion, the bitumen bond strength (BBS) test and contact angle measurements between asphalt binder and the aggregate surface by means of goniometry were used. The surface energy of the asphalt and the aggregate, with and without the presence of water, was estimated also. Testing was performed on all binders and on each binder–aggregate combination after ( a) rolling thin-film oven (RTFO) aging and ( b) RTFO and pressure aging vessel aging. The BBS results identified differences in bond strength as a result of moisture conditioning and aging. The differences depended on the aggregate source and binder type. Different failure modes were also observed (i.e., cohesive, adhesive). The results also indicated an increase in strength of adhesion associated with the aging process: the main resistance gain was observed after RTFO aging. Finally, changes in bond strength were compared with functional composition changes associated with the aging process and related to changes in performance.

The performance models in the Mechanistic–Empirical Pavement Design Guide (MEPDG), developed under NCHRP 1-37A and 1-40D, are calibrated with sections throughout the United States. Hence, it is necessary to calibrate these models for specific states and regional conditions because of the differences in materials, environmental conditions, and construction practices. In general, a pavement design based on the nationally calibrated MEPDG will result in either an overestimate or underestimate of the pavement layer thicknesses because of systematic errors arising from local differences. This deficiency calls for local calibration of the performance models in the MEPDG so that they can be used to design pavements at a regional level. The calibration procedure described in this paper concentrates on finding two bias correction factors for the asphalt concrete (AC) permanent deformation performance model after values derived from expert knowledge have been assumed for the subgrade permanent deformation calibration factors. Pavement data from the Texas Specific Pavement Study (SPS)-1 and SPS-3 experiments of the Long-Term Pavement Performance database were used to run the MEPDG and calibrate the guide to Texas conditions. The regional calibration factors were obtained by minimizing the sum of squared errors between the observed and predicted surface permanent deformation. In this case, a simultaneous joint optimization routine was applied because it was theoretically sound. Finally, an average of the regional calibration coefficients for AC and subgrade permanent deformation was computed to obtain the set of state-default calibration coefficients for Texas.

The environmental impact of road construction and rehabilitation can be associated with the increase of greenhouse gas (GHG) emissions, which are highly related to climate change. Consequently, departments of transportation have recently focused on the development and implementation of tools to evaluate the performance of projects and minimize GHG emissions. An example is the use of life cycle assessment (LCA) to analyze and quantify the environmental impact of a product, system, or process, from cradle to grave. In this regard, the present case study quantifies the carbon footprint associated with the construction of the La Abundancia–Florencia highway, located in the province of San Carlos in Costa Rica. The analysis is also intended to generate consciousness both in the public and private sectors on the environmental impacts of road construction. After an LCA study, it was determined that the construction of the hot mix asphalt (HMA) layer generates a carbon footprint of 65.8 kg of CO2e per km of road. In addition, it was evident that HMA production generates the greatest environmental impact, among all the considered LCA production and construction stages, with a GHG contribution of 38% to 39% from bitumen only. Consequently, special attention to HMA production is required in order to minimize GHG emissions.

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This paper documents and discusses the results of a study on the effects of oxidative and thermoreversible aging on the intermediate limiting phase angle temperatures for six Latin American asphalts. Materials were aged in the pressure aging vessel and investigated by infrared spectroscopy, modulated differential scanning calorimetry, and dynamic shear rheometry. It was found that the effects of irreversible/chemical and thermoreversible aging on rheological properties were of comparable weight. Hence, for future fatigue cracking performance grading, it is important to take the effects of both types of aging into consideration because the omission of thermoreversible effects will lead to inadequate control of cracking.

Accurate prediction of infrastructure condition is critical to infrastructure management agencies. Reliable and accurate predictions of infrastructure conditions save significant amounts of money for infrastructure management agencies through better planning of maintenance and rehabilitation activities. A good performance model relies heavily on the quality of the data collected and stored in the infrastructure management system. However, records of maintenance treatments are sometimes unavailable in the inventory. As a result, it is difficult to determine if a sudden increase in condition is caused by a maintenance intervention or by random measurement errors. Agencies with this problem usually filter the data set by removing possible outliers, subjecting the data to several quality control checks. For example, the change in condition may be considered a measurement error if the magnitude of the change is less than a given threshold. However, determination of the threshold value is sometimes arbitrary and may lead to bias in the development of performance models. A robust performance modeling technique was developed to address this problem. The proposed methodology captures the infrastructure deterioration rate without filtering of any data points. This method also calculates the probability that a data point is affected by maintenance intervention. Roughness data from the Long-Term Pavement Performance database is used to demonstrate the proposed methodology.