The road pavement conditions affect safety and comfort, traffic and travel times, vehicles operating cost, and emission levels. In order to optimize the road pavement management and guarantee satisfactory mobility conditions for all road users, the Pavement Management System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of pavement distress data, the possibility of data maintenance and updating, in order to evaluate the best maintenance program. In the last decade, many researches have been focused on pavement distress detection, using a huge variety of technological solutions for both data collection and information extraction and qualification. This paper presents a literature review of data collection systems and processing approach aimed at the pavement condition evaluation. Both commercial solutions and research approaches have been included. The main goal is to draw a framework of the actual existing solutions, considering them from a different point of view in order to identify the most suitable for further research and technical improvement, while also considering the automated and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the data collection and extraction to the type of distress, considering the distress detection, classification, and quantification phases of the procedure.
The unmanned aerial vehicle (UAV) photogrammetric survey of an archaeological site has proved itself to be particularly efficient. In order to obtain highly accurate and reliable results, it is necessary to design carefully the flight plan and the geo-referencing, while also evaluating the indicators of the accuracy rate. Using as a test case a UAV photogrammetric survey conducted on the archaeological site of the Roman Amphitheatre of Avella (Italy), in this paper, we propose a pipeline to assess the accuracy of the results according to some quality indicators. The flight configuration and the georeferencing chosen is then be checked via the residuals on the ground control points (GCPs), evenly distributed on the edges and over the entire area. With the aim of appraising the accuracy of the final model, we will suggest a method for the outlier detection, taking into account the statistical distribution (both global and of portion of the study object) of the reprojection errors. A filter to reduce the noise within the model will then be implemented through the detection of the angle formed by homologous rays, in order to reach a compromise between the number of the usable points and the reduction of the noise linked to the definition of the 3D model.
The surface conditions of road pavements, including the occurrence and severity of distresses present on the surface, are an important indicator of pavement performance. Periodic monitoring and condition assessment is an essential requirement for the safety of vehicles moving on that road and the wellbeing of people. The traditional characterization of the different types of distress often involves complex activities, sometimes inefficient and risky, as they interfere with road traffic. The mobile laser systems (MLS) are now widely used to acquire detailed information about the road surface in terms of a three-dimensional point cloud. Despite its increasing use, there are still no standards for the acquisition and processing of the data collected. The aim of our work was to develop a procedure for processing the data acquired by MLS, in order to identify the localized degradations that mostly affect safety. We have studied the data flow and implemented several processing algorithms to identify and quantify a few types of distresses, namely potholes and swells/shoves, starting from very dense point clouds. We have implemented data processing in four steps: (i) editing of the point cloud to extract only the points belonging to the road surface, (ii) determination of the road roughness as deviation in height of every single point of the cloud with respect to the modeled road surface, (iii) segmentation of the distress (iv) computation of the main geometric parameters of the distress in order to classify it by severity levels. The results obtained by the proposed methodology are promising. The procedures implemented have made it possible to correctly segmented and identify the types of distress to be analyzed, in accordance with the on-site inspections. The tests carried out have shown that the choice of the values of some parameters to give as input to the software is not trivial: the choice of some of them is based on considerations related to the nature of the data, for others, it derives from the distress to be segmented. Due to the different possible configurations of the various distresses it is better to choose these parameters according to the boundary conditions and not to impose default values. The test involved a 100-m long urban road segment, the surface of which was measured with an MLS installed on a vehicle that traveled the road at 10 km/h.
<p><strong>Abstract.</strong> The paper reports the results of a photogrammetric survey made using an Unmanned Aerial Vehicle (UAV) in the archaeological site of the Roman Amphitheatre in Avella (Avellino, Italy). The aim of the study is to verify which modality of image acquisition (if only nadiral images or nadiral plus Oblique images), together with the method of Global Positioning Satellite System (GNSS) survey of the Ground Control Points (GCP) is able to produce the better 3D model, in terms of accuracy, in order to extract traditional graphic drawings (plan, elevation and section), suited to the required representation scales (1<span class="thinspace"></span>:<span class="thinspace"></span>100 and 1<span class="thinspace"></span>:<span class="thinspace"></span>50). The accuracy in georeferencing was evaluated analysing the residues on the GCPs; subsequently, a more detailed analysis of the accuracy of the final 3D model was performed analysing the residuals on the image coordinates, also called re-projection error. The method developed is based on the statistical analysis of the different models, built changing the GCPs survey method and the photogrammetric shots acquired. The results of our analysis show that the photogrammetric survey is more ‘stable’ using only nadiral images and that the nRTK technique allows results comparable to those obtained with static measurements, both in precision and in reliability. Moreover, if the GCPs are measured in nRTK mode, taking into consideration the graphical error, the maximum representation scale is 1<span class="thinspace"></span>:<span class="thinspace"></span>100, whereas the use of static technique makes it possible to describe major details, at a scale of 1<span class="thinspace"></span>:<span class="thinspace"></span>50.</p>
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