• Energy Research

Environmental safeguards promote innovative construction technologies for sustainable pavements. On these premises, this study investigated four hot mix asphalt (HMA) mixtures—i.e., A, B, C, and D—for the railway sub-ballast layer with 0%, 10%, 20%, and 30% reclaimed asphalt pavement (RAP) by total aggregate mass and a rejuvenator additive, varying the bitumen content between 3.5% and 5.0%. Both Marshall and gyratory compactor design methods have been performed, matching the stability, indirect tensile strength, and volumetric properties of each mixture. Dynamic stiffness and fatigue resistance tests provided mechanical performances. Laboratory results highlighted that the RAP and the rejuvenator additive increase the mechanical properties of the mixtures. In addition, the comparative analysis of production costs revealed up to 20% savings as the RAP content increased, and the life cycle impact analysis (LCIA) proved a reduction of the environmental impacts (up to 2% for resource use-fossils, up to 7% for climate change, and up to 13% for water use). The experimental results confirm that HMA containing RAP has mechanical performances higher than the reference mixture with only virgin raw materials. These findings could contribute to waste management and reduce the environmental and economic costs, since the use of RAP in the sub-ballast is not, so far, provided in the Italian specifications for railway construction.

In the process of road pavement health and safety assessment, crack detection plays a pivotal role in a preventive maintenance strategy. Recently, Convolutional Neural Networks (CNNs) have been applied to automatically identify the cracks on concrete pavements. The effectiveness of a CNN-based road crack detection and measurement method depends on several factors, including the image segmentation of cracks with complex topology, the inference of noises with similar texture to the distress, and the sensitivity to thin cracks. The presence of shadows, strong light reflections, and road markings can also severely affect the accuracy in detection and measurement. In this study, a review of the state-of-the-art CNN methods for crack identification is presented, paying attention to existing limitations. Then, a novel deep residual convolutional neural network (Parallel ResNet) is proposed with the aim of creating a high-performance pavement crack detection and measurement system. The challenge and special feature of Parallel ResNet is to remove the noise inference, identifying even thin and complex cracks correctly. The performance of Parallel ResNet has been investigated on two publicly available datasets (CrackTree200 and CFD), comparing it with that of competing methods suggested in the literature. Parallel ResNet reached the maximum scores in Precision (94.27%), Recall (92.52%), and F1 (93.08%) using the CrackTree200 dataset. Similarly, for the CFD dataset the novel method achieved high values in Precision (96.21%), Recall (95.12%), and F1 (95.63%). Based on the crack detection and image recognition results, mathematical morphology was then used to further minimize noise and accurately segment the road diseases, obtaining the outer contours of the connected domain in crack images. Therefore, crack skeletons have been extracted to measure the distress length, width, and area on images of rigid pavements. The experimental results show that Parallel ResNet can effectively minimize noise to obtain the geometry of cracks. The results of crack characteristic measurements are accurate and Parallel ResNet can be assumed as a reliable method in pavement crack image analysis, in order to plan the best road maintenance strategy.

Stone pavements are the historical, architectural, and cultural heritage of lots of cities in Italy and the world. Road managers should be able to make decisions on the global conditions to define the most suitable strategies and maintenance interventions for every type of pavement. There are no standard monitoring methods or criteria for evaluating stone pavement performance. These pavements have more uneven surfaces than traditional pavements, but this characteristic could be accepted if type of vehicles and relative travel conditions are considered. Therefore, it is useful to define criteria for assessing roughness considering the comfort experienced by users in different vehicles. In this research, both traditional and innovative methodologies for assessing irregularities have been investigated using true stone surface profiles. In this regard, traditional performance indicators such as the International Roughness Index (IRI) defined by the ASTM E1926, the ISO 8608 classification, and the frequency-weighted vertical acceleration (awz) provided by ISO 2631-1 for comfort assessment have been considered. In the case of comfort assessment, three dynamic vehicle models (bike, automobile, and bus) have been adopted. Finally, this two-part paper also proposes an innovative straightedge analysis for stone pavements (SASP) to evaluate the effect on traffic of both pavement profile roughness and localized irregularities. In this way, the authors aim to provide an effective tool to monitor stone pavements.