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.

Increasing environmental awareness is pushing towards sustainable approaches to the design and management of transport infrastructures. A life cycle assessment of low noise pavements is carried out here, with the aim to evaluate and compare the use of warm mix asphalts containing crumb rubber (CR) from end-of-life tires (ELTs) and reclaimed asphalt pavement (RAP). Different scenarios have been considered, taking into account production, construction, maintenance activities, and end-of-life of the pavement, according to a cradle to grave approach. Hot mix asphalt (HMA) was used as a reference wearing course. Results show that the simultaneous implementation of warm asphalt technologies and recycled materials can lead to a 50% reduction of the environmental burdens, compared to the standard scenario. The difference is mainly ascribed to the material depletion, the energy consumption, and the emissions associated with the frequency of maintenance of the wearing course. The use of asphalt rubber is environmentally advantageous, if compared to polymer modified binders (PMB); moreover, rubberized open-graded mixtures require the lowest bitumen content and maintenance. The findings of this research support the use of recycled materials and warm technologies as a way to improve the environmental sustainability of low noise pavements.

Tyre/road noise is a complex phenomenon which is function of tyre, surface properties and speed. This study aims to define a rolling noise prediction model for a reference tyre, as a function of speed, on dense asphalt surfaces with medium-low macrotexture levels. The model was defined by relating texture levels of pavement profiles and rolling noise levels recorded at different speeds. The study also defines an extension of the model valuable for semi-porous surfaces, as a function of only surface characteristics. Both of the models serve as a support tool for mix design of low noise asphalt pavement surfaces.

Existing pavements of collector rural roads are often characterized by a strong dishomogeneity in pavement layer thicknesses and mechanical properties of materials. In order to evaluate structural conditions of these pavements, they must be delineated in homogeneous sub-sections with regard to variations both in the thickness and the mechanical properties of pavement layers, but this task is often very difficult. For this purpose, this paper proposes a statistical model for the straight evaluation of critical strains in pavements by using the deflections measured by the Falling Weight Deflectometer and the layer thicknesses without backcalculating layer moduli. The model is calibrated on the basis of experimental data and it is useful to evaluate statistical parameters of the homogeneous sub-sections with the aim to evaluate the residual pavement life taking into account the reliability concepts.

This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical displacement (in terms of mm/year) of road pavements as a result of exogenous phenomena occurrence, such as subsidence. Such algorithms are based on surveys performed with Persistent Scatterer Interferometric SAR (PS-InSAR) over an area of 964 km² in the Tuscany Region, Central Italy. Starting from this basis, in this paper, we propose to integrate the information provided by these MLAs with 10 km of in situ profilometric measurements of the pavement surface roughness and relative calculation of the International Roughness Index (IRI). Accordingly, the aim is to appreciate whether and to what extent there is an association between displacements estimated by MLAs and IRI values. If a dependence exists, we may argue that road regularity is driven by exogenous phenomena and MLAs allow for the replacement of in situ surveys, saving considerable time and money. In this research framework, results reveal that there are several road sections that manifest a clear association among these two methods, while others denote that the relationship is weaker, and in situ activities cannot be bypassed to evaluate the real pavement conditions. We could wrap up that, in these stretches, the road regularity is driven by endogenous factors which MLAs did not integrate during their training. Once additional MLAs conditioned by endogenous factors have been developed (such as traffic flow, the structure of the pavement layers, and material characteristics), practitioners should be able to estimate the quality of pavement over extensive and complex road networks quickly, automatically, and with relatively low costs.