Infrastructures, Volume 9, Issue 5 (May 2024) – 2 articles

Among the technologies proposed for achieving carbon neutralization in asphalt road pavements, warm-mix asphalt (WMA) has garnered increasing attention in recent years. While WMA holds the potential for various environmental and technical benefits, a comprehensive understanding of its implementation, technology selection, and additives is essential for successful application. This study presents a case where a bio-based chemical additive was employed to produce WMA containing 50% reclaimed asphalt pavement (RAP) for a surface course in Bern, Switzerland. To minimize additional variables during testing and analysis, no other additive or rejuvenator was introduced into the mixtures. The testing plan encompassed laboratory tests on samples collected during material placement and recompacted at varying temperatures in the laboratory, as well as cores extracted from the job site. As anticipated, the presence of the chemical WMA additive did not alter the rheological properties of the reference bitumen. Although in the mixture-scale tests, the WMA mixture exhibited comparable properties to the control hot-mix asphalt (HMA), it is not expected that the small dosage of the chemical additive functions the same grade after reheating and compaction. Nevertheless, the cores extracted from the job site proved the efficiency of the applied WMA technology. In addition, consistent with existing literature, the cracking tolerance (CT) index values of 62 for HMA and 114 and 104.9 for WMA mixtures indicated that the latter is less susceptible to cracking. Consequently, this characteristic could contribute to the enhanced durability of asphalt pavements. Full article

This research utilizes the Long-Term Pavement Performance database, focusing on devel-oping a predictive model for flexible pavement performance in the Southern United States. Analyzing 367 pavement sections, this study investigates crucial factors influencing asphaltic concrete (AC) pavement deterioration, such as structural and material components, air voids, compaction density, temperature at laydown, traffic load, precipitation, and freeze–thaw cycles. The objective of this study is to develop a predictive machine learning model for AC pavement wheel path cracking (WpCrAr) and the age at which cracking initiates (WpCrAr) as performance indicators. This study thoroughly investigated three ensemble machine learning models, including random forest, extremely randomized trees (ETR), and extreme gradient boosting (XGBoost). It was observed that XGBoost, optimized using Bayesian methods, emerged as the most effective among the evaluated models, demonstrating good predictive accuracy, with an R² of 0.79 for WpCrAr and 0.92 for AgeCrack and mean absolute errors of 1.07 and 0.74, respectively. The most important features influencing crack initiation and progression were identified, including equivalent single axle load (ESAL), pavement age, number of layers, precipitation, and freeze–thaw cycles. This paper also showed the impact of pavement material combinations for base and subgrade layers on the delay of crack initiation. Full article