Recycled and low-temperature materials are promising solutions to reduce the environmental burden deriving from hot mix asphalts. Despite this, there is lack of studies focusing on the assessment of the life-cycle impacts of these promising technologies. Consequently, this study deals with the life cycle assessment (LCA) of different classes of pavement technologies, based on the use of bituminous mixes (hot mix asphalt and warm mix asphalt) with recycled materials (reclaimed asphalt pavements, crumb rubber, and waste plastics), in the pursuit of assessing energy and environmental impacts. Analysis is developed based on the ISO 14040 series. Different scenarios of pavement production, construction, and maintenance are assessed and compared to a reference case involving the use of common paving materials. For all the considered scenarios, the influence of each life-cycle phase on the overall impacts is assessed to the purpose of identifying the phases and processes which produce the greatest impacts. Results show that material production involves the highest contribution (about 60–70%) in all the examined impact categories. Further, the combined use of warm mix asphalts and recycled materials in bituminous mixtures entails lower energy consumption and environmental impacts due to a reduction of virgin bitumen and aggregate consumption, which involves a decrease in the consumption of primary energy and raw materials, and reduced impacts for disposal. LCA results demonstrate that this methodology is able to help set up strategies for eco-design in the pavement sector.

Low-volume roads (LVRs), such as rural, farm-to-market, and less-used local and city roads, are an important part of the world's transportation infrastructure. LVRs have been credited as a direct cause of the socioeconomic development of rural communities. It has been estimated that 60% of the road network in the United States is made up of low-volume roads. The construction, maintenance, and rehabilitation of these roads are major tasks that result in about 54% of the total annual expenditure of transportation agencies in the United States. Better design and construction methods will lead to lower maintenance and rehabilitation costs of LVRs. Stabilization of weak subgrade soils to support LVRs is a widely accepted method of improving their performance. However, the selection of a stabilization alternative on the basis of cost–benefit analysis is a crucial task for a transportation agency and one that has not been addressed in a systematic manner. In this paper, a new conceptual engineering economics tool–based life-cycle cost analysis (LCCA) is developed to optimize and to select the best stabilizer and the stabilization technique for a given subgrade soil and given traffic conditions. In this analysis, agency, user, and externality costs are addressed. Two case studies are analyzed for European and U.S. road conditions to validate the LCCA model. Results demonstrate that, under specific boundary conditions, soil stabilization can play an important role, merging the environmental and mechanical effectiveness of low-volume roads.

The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies.