Green public procurement criteria for road infrastructures: State of the art and proposal of a weighted sum multicriteria analysis to assessenvironmental impacts

In the last years, the attention to environmental issue is growing, demonstrating the interest to protect the nature and to better use the non-renewable resources. At international level, and especially in the European Community, for different trades, a wide production of voluntary documents and institutional acts proves the interest and the need for a green economy. An innovative approach may lead to the experience of Green Public Procurements (GPP), to protect the environment as a public interest and to promote technological developments. So far, the experiences of GPP are limited, not entirely positive and in the field of road infrastructures almost entirely absent. Construction and maintenance of road infrastructures is objectively more complex than purchasing goods or services. The paper proposes the integration of the weighted sum multi-criteria analysis into existing procedures. The methodology needs for environmental labels related to materials, machines and works which contribute to the final product "road". The labels are recognized at international level and consistent with procedures, conditions and criteria currently published in road tenders, therefore the approach can be followed to pursue the environmental sustainability of road infrastructures without compromising the economic attention

Cold asphalt contaning 100% reclamed asphalt. A sustainable technology for cycle paths and maintenance intervations

Both the National Recovery and Resilience Plan (Next Generation EU Program) and the development strategies for Smart Cities focus on cycle and pedestrian paths. Their pavements must be safe, durable and sustainable and considering the need to preserve the resources that Planet Earth offers to humans, it is essential to opt for innovative construction technologies that allow recycling methods without necessarily involving the addition of first-use materials. In the field of road infrastructure, the recovery of material deriving from the demolition of old pavements (RA - Reclaimed Asphalt) is only possible thanks to the use of specific products. A state-of-the-art rejuvenator is currently being used for the construction of cycling paths with 100% cold-mixed RA. This product is currently being studied for the INFRAROB project: “Maintaining integrity, performance and safety of the road infrastructure through autonomous robotized solutions and modularization” (Horizon 2020) with particular reference to “potholes patching” materials. Some technical data of the experiences developed to date are shown below

ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation

Maintenance has a major impact on the financial plan of road managers. To ameliorate road conditions and reduce safety constraints, distress evaluation methods should be efficient and should avoid being time consuming. That is why road cadastral catalogs should be updated periodically, and interventions should be provided for specific management plans. This paper focuses on the setting of an Unmanned Ground Vehicle (UGV) for road pavement distress monitoring, and the Rover for bituminOus pAvement Distress Survey (ROADS) prototype is presented in this paper. ROADS has a multisensory platform fixed on it that is able to collect different parameters. Navigation and environment sensors support a two-image acquisition system which is composed of a high-resolution digital camera and a multispectral imaging sensor. The Pavement Condition Index (PCI) and the Image Distress Quantity (IDQ) are, respectively, calculated by field activities and image computation. The model used to calculate the I-ROADS index from PCI had an accuracy of 74.2%. Such results show that the retrieval of PCI from image-based approach is achievable and values can be categorized as "Good"/"Preventive Maintenance", "Fair"/"Rehabilitation", "Poor"/"Reconstruction", which are ranges of the custom PCI ranting scale and represents a typical repair strategy

Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement

Automatic crack detection on road pavements using encoder-decoder architecture

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low-level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms

Materials study to implement a 3D printer system to repair road pavement potholes

InfraRob is a research project funded by the European Commission's research programme Horizon 2020 that aims to maintain integrity, performance, and safety of the road infrastructure through autonomous robotized solutions and modularization. A specific task of the project is focused on the development of a system 3D printer able to extrude a specific mixture for filling in small cracks and potholes, to be integrated with an existing small autonomous carrier. The first step of the research deals with the definition of the optimal parameters of the system 3D printer/mixture, by studying in parallel the material design and the printer design. This paper presents the study performed on a mixture chosen among those commonly used for road potholes repair. The mixture is studied to achieve and balance the different conflicting performances: consistence, flowability homogeneity, and internal structure. In addition to the basic components, the use of special additives has also been explored to improve the plasticity and adhesivity of the mixture. The first phase of tests is conducted to define the main printing controls: i) Extrudability control: materials for 3D printing need to have an acceptable degree of extrudability, which is related to the capacity of a material to pass continuously through the printing head; ii) Flowability control, to ensure the mixture can be easy-pumpable in the delivery system and easy-usable on the crack or the pothole to be filed-in; iii) Setting time control: printing material requires a certain setting time to maintain a consistent flow rate for good extrudability, thus appropriate additives are needed to control the setting time. The second phase includes in situ tests to verify the compaction of the mixture under the traffic loads. The paper presents the results of the lab and in situ tests, and the features of the chosen mix, suitable to be managed by the 3D printer

Sentient Spaces: Intelligent Totem Use Case in the ECSEL FRACTAL Project

The objective of the FRACTAL project is to create a novel approach to reliable edge computing. The FRACTAL computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes). The node will also have the capability of learning how to improve its performance against the uncertainty of the environment. In such a context, this paper presents in detail one of the key use cases: an Internet-of-Things solution, represented by intelligent totems for advertisement and wayfinding services, within advanced ICT-based shopping malls conceived as a sentient space. The paper outlines the reference scenario and provides an overview of the architecture and the functionality of the demonstrator, as well as a roadmap for its development and evaluation

Bioactive potential of minor italian olive genotypes from apulia, sardinia and abruzzo

This research focuses on the exploration, recovery and valorization of some minor Italian olive cultivars, about which little information is currently available. Autochthonous and unexplored germplasm has the potential to face unforeseen changes and thus to improve the sustainability of the whole olive system. A pattern of nine minor genotypes cultivated in three Italian regions has been molecularly fingerprinted with 12 nuclear microsatellites (SSRs), that were able to unequivocally identify all genotypes. Moreover, some of the principal phenolic compounds were determined and quantified in monovarietal oils and the expression levels of related genes were also investigated at different fruit developmental stages. Genotypes differed to the greatest extent in the content of oleacein (3,4-DHPEA-EDA) and total phenols. Thereby, minor local genotypes, characterized by stable production and resilience in a low-input agro-system, can provide a remarkable contribution to the improvement of the Italian olive production chain and can become very profitable from a socio-economic point of view

Proteomic profiling of retinoblastoma-derived exosomes reveals potential biomarkers of vitreous seeding

Retinoblastoma (RB) is the most common tumor of the eye in early childhood. Although recent advances in conservative treatment have greatly improved the visual outcome, local tumor control remains difficult in the presence of massive vitreous seeding. Traditional biopsy has long been considered unsafe in RB, due to the risk of extraocular spread. Thus, the identification of new biomarkers is crucial to design safer diagnostic and more effective therapeutic approaches. Exosomes, membrane-derived nanovesicles that are secreted abundantly by aggressive tumor cells and that can be isolated from several biological fluids, represent an interesting alternative for the detection of tumor-associated biomarkers. In this study, we defined the protein signature of exosomes released by RB tumors (RBT) and vitreous seeding (RBVS) primary cell lines by high resolution mass spectrometry. A total of 5666 proteins were identified. Among these, 5223 and 3637 were expressed in exosomes RBT and one RBVS group, respectively. Gene enrichment analysis of exclusively and differentially expressed proteins and network analysis identified in RBVS exosomes upregulated proteins specifically related to invasion and metastasis, such as proteins involved in extracellular matrix (ECM) remodeling and interaction, resistance to anoikis and the metabolism/catabolism of glucose and amino acids