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The topographical features of a site and the anthropogenic artefacts inside and outside the airport boundaries influence the infrastructure use. Objects penetrating the obstacle limitation surfaces (OLS) or standing outside those surfaces have to be mapped and risk-assessed because they could be a hazard to air navigation. This study aims to quantify the risk of collision between aircraft and obstacles in the airspace. There are no available procedures in the literature: the authors supposed that the obstacle type and the examined OLS affect the collision risk. The proposed risk values and amplification factors derive from interviews with technicians. The methodology has been implemented in an existing airport with 589 penetrating obstacles: the results highlight that 69.8% of obstacles imply a negligible risk, and 3.7% require further analyses by the competent aviation authority. In this study, buildings and pylons penetrating the Transitional Surface are the most hazardous obstacles.

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Repairing potholes is a task for municipalities to prevent serious road user injuries and vehicle damage. This study presents a low-cost, high-performance pothole monitoring system to maintain urban roads. The authors developed a methodology based on photogrammetry techniques to predict the pothole's shape and volume. A collection of overlapping 2D images shot by a Raspberry Pi Camera Module 3 connected to a Raspberry Pi 4 Model B has been used to create a pothole 3D model. The Raspberry-based configuration has been mounted on an autonomous and remote-controlled robot (developed in the InfraROB European project) to reduce workers' exposure to live traffic in survey activities and automate the process. The outputs of photogrammetry processing software have been validated through laboratory tests set as ground truth; the trial has been conducted on a tile made of asphalt mixture, reproducing a real pothole. Global Positioning System (GPS) and Geographical Information System (GIS) technologies allowed visualising potholes on a map with information about their centre, volume, backfill material, and an associated image. Ten on-site tests validated that the system works in an uncontrolled environment and not only in the laboratory. The results showed that the system is a valuable tool for monitoring road potholes taking into account construction workers' and road users' health and safety.

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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 IROADS 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.

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The Cement Grouted Bituminous Mix (CGBM) is an innovative material that could be used to build airport pavements subjected to heavy concentrated loads or fuel and solvent leaks. CGBM is composed of a porous asphalt clogged with an expansive cement mixture, which fills the asphalt voids. This paper focuses on two airport pavements (i.e., a taxiway and a helipad one) to be paved in an Italian airport. For each surface, the construction and maintenance costs of a CGBM pavement and a traditional flexible pavement have been compared. The pavements should bear different traffic loads, while the weather, subgrade, and materials are the same: the fatigue and rutting verification gives structures whose cost analysis leads to different results. The CGBM solution for the taxiway has a cost comparable to that of the equivalent traditional flexible pavement (i.e., 73.87 €/m2 vs. 73.20 €/m2 during the service life). On the other hand, the overall discounted cost of the helipad surface paved with CGBM is higher than that obtained for the traditional pavement (i.e., 82.4 €/m2 vs. 67.5 €/m2). Therefore, the study demonstrates that the economic opportunity of CGBM solutions strongly depends on traffic loads.

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Cool pavements are reflective and/or permeable pavements that improve microclimate of urban areas where heat islands cause discomfort to citizens. Stone pavements lower surface temperatures and reduce the amount of heat absorbed. This study assessed, using ENVI-met 4.3 LITE software, how air temperature and predicted mean vote depend on physical properties of the road pavement. A comparative microclimatic analysis was implemented on a rectangular square in Rome (Italy) in the summer, paved in three different ways: asphalt, traditional sampietrini, and permeable sampietrini. The model considered local weather parameters, surrounding fabric, and vegetation to give reliable results in terms of numerical and graphical output using the application tool Leonardo. The tested pavement types affected air temperature during the day, but did not influence this variable in the early morning. Permeable sampietrini pavement was more effective than traditional sampietrini pavement in reducing air temperature compared to the current asphalt surface. The road pavement did not, however, affect human comfort in terms of predicted mean vote. The obtained results are useful for further investigation of parameters that could modify the microclimatic conditions of urban areas.

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Airport pavements should ensure regular and safe movements during their service life; the management body has to monitor the functional and structural characteristics, and schedule maintenance work, balancing the often conflicting goals of safety, economic and technical issues. This paper presents a remote monitoring system to evaluate the structural performance of a runway composed of concrete thresholds and a flexible central runway. Thermometers, strain gauges, and pressure cells will be embedded at different depths to continuously monitor the pavement's response to traffic and environmental loads. An innovative system allows data acquisition and processing with specific calculation models, in order to inform the infrastructure manager, in real time, about the actual conditions of the pavement. In this way, the authors aim to develop a system that provides useful information for the correct implementation of an airport pavement management system (APMS) based on real-life data. Indeed, it permits comprehensive monitoring functions to be performed, based on the embedded sensing network.

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Air traffic controllers aim to optimize airport capacity, that is to increase the number of aircraft movements per hour maintaining a limited delay. There are several definitions of capacity, which depend on the considered airport element. This study focused on the development of a method that allows evaluating the impact of tower air traffic controllers' workload on airport capacity. It adapts a model for the workload of sector controllers designed by Eurocontrol to tower controllers and tests it on a heavily busy international airport. In order to collect controllers' working times, a campaign of data collection has been carried out from the radio frequency occupation. The results allowed us to extrapolate the hourly percentage of work of the various tower controllers using a fast-time simulation software. By imposing an hourly working threshold on tower air traffic controllers, it was possible to obtain a maximum number of manageable aircraft, which was compared with the airside capacity of the airport. The results show that the maximum traffic manageable from the airside would produce unacceptable workload for tower controllers, highlighting the link between airport capacity and the human component.

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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.

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The attention to sustainability-related issues has grown fast in recent decades. The experience gained with these themes reveals the importance of considering this topic in the construction industry, which represents an important sector throughout the world. This work consists on conducting a multicriteria analysis of four cement powders, with the objective of calculating and analysing the environmental, human health and socio-economic effects of their production processes. The economic, technical, environmental and safety performances of the examined powders result from official, both internal and public, documents prepared by the producers. The Analytic Hierarchy Process permitted to consider several indicators (i.e., environmental, human health related and socio-economic parameters) and to conduct comprehensive and unbiased analyses which gave the best, most sustainable cement powder. As assumed in this study, the contribution of each considered parameter to the overall sustainability has a different incidence, therefore the procedure could be used to support on-going sustainability efforts under different conditions. The results also prove that it is not appropriate to regard only one parameter to identify the 'best' cement powder, but several impact categories should be considered and analysed if there is an interest for pursuing different, often conflicting interests.

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