This paper proposes an ensemble learning model that deploys a Gradient Boosting Decision Tree (GBDT) to predict two relevant functional indices, the International roughness index (IRI) and the rut depth (RD), considering multiple influence factors. To train and validate the proposed models, more than 1600 different records were extracted from Long-Term Pavement Performance database. The most suitable hyper parameters for the GBDT model are determined through a grid search and 5-fold cross-validation. Then, a sensitivity analysis is performed to determine the final input variables among the initial considered factors. Further, the optimized models utilise SHAP (Shapley Additive explanation) to interpret the results and analyse the importance of influencing factors. Finally, a comparison experiment with reference artificial intelligence approaches demonstrates that, the GBDT model can outperform the artificial neural network (ANN) and the random forest regression (RFR) methods in terms of quality of prediction results, reaching a coefficient of determination (R²) equal to 0.9. The proposed model can provide more precise pavement performance values and may be useful for providing accurate reference for pavement maintenance and optimising the available budget for road administrations.

Road construction and maintenance have a great impact on the environment, owing to the huge volumes of resources involved. Consequently, current production procedures and technologies must be properly investigated, for identifying and quantifying the life cycle environmental impacts produced. In this paper, primary data, i.e., site-specific data directly collected or measured on a reference plant, are analyzed for calculating the impact of the production of a hot mix asphalt. The analysis is performed in a from “cradle to gate” approach to estimate the environmental burdens of the production process in an average plant, representative of the existing technology in Italy and Southern Europe. The research outcomes are useful to increase reliability in quantification of asphalt production impacts and the contribution of each component. The results represent a reference basis for producers, designers, and contractors in the decisional phases, identifying the most critical aspects in the current practice and the possible improvements for reducing impacts of road industries. In this regard, efficient energy technologies for reducing the production temperature (such as warm mix asphalt) and burned fuels are proven to assure relevant improvements in the environmental performance.

Road pavements are subject to various forms of degradation compromising their functionality with negative effects on safety. For assuring the highest quality, all the distresses have to be properly identified and quantified by road administrators. For increasing efficiency and reducing costs and times of surveys, several innovative methods to detect, classify and measure surface distresses were proposed, with variable results. In this context, the authors propose an algorithm for automated pothole detection through the processing of 3D data of pavement surfaces, acquired using an innovative high-performance equipment. The algorithm, derived from computer vision, is able of identifying potholes in road sections, assuring a reliable estimation of shape and severity, in terms not only of area, perimeter, but also depth, with practical benefits. The numerical results show the remarkable performance of the proposed algorithm, even compared to alternative traditional methodologies. In terms of Precision, Recall and F-Score, it assures mean values equal respectively to 89.75%, 92.95% 91.28%. Validation was also performed in terms of area error rate, with an average value of 5.15%, significantly lower than other approaches. Then, the algorithm represents a reliable alternative to traditional approaches and allows road administrators to derive data to optimize maintenance and road functionality.

In recent years, the growing survey capability for continuous and frequent inspections of road pavement conditions has raised questions regarding the optimal solutions for data management. Considering traditional approaches, classical Pavement Management Systems seem inadequate for exploiting the potential of similar data, especially for visualization and infrastructure modelling. The development of BIM procedures may be helpful although the existing solutions are not ready for handling road condition and maintenance management. Furthermore, the recent international regulations gradually adopt BIM in all infrastructure projects, as reference model. Then, as a preliminary approach, in this paper, a solution for presenting and elaborating pavement condition information in an I-BIM environment is proposed. This procedure simplifies representation and elaboration of survey information regarding structural and functional conditions of pavements in a realistic 3D environment, exploiting smart object potential. Specific routines have been coded for upgrading and elaborating specific extended attributes related to pavement condition. This approach represents an important evolution compared to traditional methodologies, as a modern and ‘user-friendly’ decision support tool for road agencies, combining in a single environment all the life cycle phases of the infrastructure. The proposed approach has been tested, in a preliminary form, on an existing motorway, with positive results.

Recently, even the road industry has been involved in an evolutionary process, inspired by the novel ‘Industry 4.0’. This transition moves the attention towards big-data and Internet of Things concepts, determining novel data flows and relevant management efforts. In smart roads, modern instrumented vehicles and networks of sensors will provide frequent helpful measures (traffic, weather, condition, accidents, mechanical performance, etc.), with reduced efforts and costs. However, this process is currently at the preliminary phases and several issues raise. Since the management and elaboration of huge amounts of data represent a relevant novel issue, in this study, an original web platform for collection and analysis of road performance data is proposed. This platform can acquire and process several data classes and support maintenance activity planning. This paper focuses on pavement maintenance, to provide a reliable decision support tool for road agencies, alternatively feedable by modern survey equipment and, in future, widespread sensors (when effective smart road sensors are installed on the main highways). The platform has been tested, in a preliminary form, on an existing motorway, considering high-performance survey systems data, with interesting and positive results.

The development of the road construction sector determines the consequences on consumption of non-renewable resources, energy expenditure and environmental pollution. Recent sustainability issues have highlighted the importance of efficient design and quality-oriented techniques in this sector, due to the huge amount of materials involved in construction and maintenance activities. Thus, it is necessary to properly quantify the environmental impacts of asphalt mixtures used for pavement construction, considering the whole life cycle of the products. Life cycle assessment (LCA) represents the most appropriate methodological framework for assessing the environmental burdens of a product, from raw material acquisition to final disposal. A common problem for LCA is the lack of primary data useful to calculate the product eco-profile, for a specific production process. In this context, there is generally limited reliable and accurate data regarding the asphalt plant production phase, which represents the most critical phase. Consequently, the aim of this paper is to perform an environmental/energy audit of an asphalt plant and, further, to collect and analyze primary data useful for the definition of the eco-profile of 1 metric ton of hot mix asphalt (HMA), following a “gate to gate” approach, including transport. The asphalt production is examined in a Sicilian batch-mix plant, representing one of the most commonly used for asphalt production in the Italian context. The results are of interest for asphalt mixture producers, contractors, transportation agencies and researchers seeking to quantify asphalt pavement environmental impacts in Italy, based on context-related foreground data.