Polymer additives are used to improve the properties of road bitumens including their oxidative resistance. However, their usage as anti-oxidative materials remains relatively unclear. This study aims to investigate the changes in the morphology and the rheological response of polymer modified bitumens used in road pavement construction caused by ageing. An elastomer (radial styrene butadiene styrene, SBS) and a plastomer (ethyl vinyl acetate, EVA) polymer were mixed with one base bitumen at three polymer concentrations. The bitumens were RTFO and PAV aged. The morphology of the bitumens was captured by fluorescence microscopy while the rheological properties were measured by means of the multiple stress creep and recovery (MSCR) test. The results show that the morphology of the SBS modified bitumen degrades with ageing as a function of polymer concentration and dispersion, with higher dispersion being more resistant. The morphology of the EVA modified bitumen has a low ageing susceptibility irrespective of polymer concentration. The MSCR response of EVA modified bitumens does not differ from that found for unmodified bitumen, where the hardening produces a decrease in the non-recoverable compliance. In the case of SBS modified bitumen, the degradation of the polymer backbone affects the bitumen hardening as much as the polymer phase dispersed and networked in the bitumen phase. Furthermore, in the case of the elastomer, the average percent recovery is in agreement with the variation of the morphology with ageing. Therefore, the use of the average percent recovery as a valuable rheological index of the integrity of the polymer network can be advocated.

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km2), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities.

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This paper proposes a tool to optimize the mix design of low-noise pavements. An experimental model was developed to predict the rolling noise of a reference car tire as a function of the composition and volumetric characteristics of mixes obtained from in-service pavements. The model enables an analyst to identify which composition parameters need to be altered to improve the acoustic performance of a low-noise pavement. To define the experimental model, several types of asphalt surface layers composed of hot-mix asphalt mixtures with different void contents, different aggregate grading, and different bitumen percentages were analyzed in situ and by laboratory tests. The acoustical properties of pavement surfaces were evaluated by the close-proximity method. The model was defined by using a multivariate nonlinear regression technique to relate composition and volumetric characteristics of asphalt mixtures with rolling noise levels recorded at different speeds. This model, which is a function of several significant parameters of asphalt mixture composition and tire speed, has proved to be highly reliable in predicting car tire rolling noise. Because the model enables the identification of mixture characteristics that require modification in relation to the specific value of the mean traffic speed, it is particularly useful for the optimization of low-noise pavement mix design.

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