A Robotized Raspberry-Based System for Pothole 3D Reconstruction and Mapping.

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.

Development of a GIS-Based Methodology for the Management of Stone Pavements Using Low-Cost Sensors.

Stone pavements are present in many cities and their historical and cultural importance is well recognized. However, there are no standard monitoring methods for this type of pavement that allow road managers to define appropriate maintenance strategies. In this study, a novel method is proposed in order to monitor the road surface conditions of stone pavements in a quick and easy way. Field tests were carried out in an Italian historic center using accelerometer sensors mounted on both a car and a bicycle. A post-processing phase of that data defined the comfort perception of the road users in terms of the awz index, as described in the ISO 2631 standard. The results derived from the dynamic surveys were also compared with the corresponding values of typical pavement indicators such as the International Roughness Index (IRI) and the Pavement Condition Index (PCI), measured only on a limited portion of the urban road network. The network's implementation in a Geographic Information System (GIS) represents the surveys' results in a graphical database. The specifications of the adopted method require that the network is divided into homogeneous sections, useful for measurement campaign planning, and adopted for the GIS' outputs representation. The comparisons between IRI-awz (R2 = 0.74) and PCI-awz (R2 = 0.96) confirmed that the proposed method can be used reliably to assess the stone pavement conditions on the whole urban road network.

Pavement Distress Estimation via Signal on Graph Processing.

A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road pavement distresses is presented with the aim of improving automatic pavement distress detection systems. A novel nonlinear Bayesian estimator in recovering distress metrics is also derived. The performance of the methodology was evaluated on a large dataset of pavement distress values collected in field tests conducted in Kazakhstan. The application of the proposed methodology is effective in recovering acquisition errors, improving road failure detection. Moreover, the output of the Bayesian estimator can be used to identify sections where the measurement acquired by the 3D laser technology is unreliable. Therefore, the presented model could be used to schedule road section maintenance in a better way.

Technical Proposal for Monitoring Thermal and Mechanical Stresses of a Runway Pavement.

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.

Validation of a Low-Cost Pavement Monitoring Inertial-Based System for Urban Road Networks.

Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, a novel inertial sensor-based system is proposed using a low-cost inertial measurement unit (IMU) and a global positioning system (GPS) module, which are connected to a Raspberry Pi Zero W board and embedded inside a vehicle to indirectly monitor the road condition. To assess the level of pavement decay, the comfort index awz defined by the ISO 2631 standard was used. Considering 21 km of roads with different levels of pavement decay, validation measurements were performed using the novel sensor, a high performance inertial based navigation sensor, and a road surface profiler. Therefore, comparisons between awz determined with accelerations measured on the two different inertial sensors are made; in addition, also correlations between awz, and typical pavement indicators such as international roughness index, and ride number were also performed. The results showed very good correlations between the awz values calculated with the two inertial devices (R2 = 0.98). In addition, the correlations between awz values and the typical pavement indices showed promising results (R2 = 0.83-0.90). The proposed sensor may be assumed as a reliable and easy-to-install method to assess the pavement conditions in urban road networks, since the use of traditional systems is difficult and/or expensive.

Use of Hybrid Mineral Filler with High Emissivity in Asphalt Mixture for Cooling Road Pavements.

Road asphalt pavements cover a high percentage of urban size and contribute to heat islands. This study proposed a new method to cool asphalt pavement by incorporating a kind of hybrid mineral filler (HMF) with high emissivity into a reference asphalt mixture prepared with limestone mineral filler (LMF). The physical, emissive, solar reflective, and rheological properties of asphalt mastic and the thermal performances of asphalt mixture were covered to investigate the possibility of the proposed strategy. From Fourier transform infrared spectrum test, it can be found that HMF was physically blended with asphalt. The emissivity results show that HMF increased the emissivity of asphalt mastic from 0.9204 to 0.9820. The asphalt mastic containing HMF had similar solar reflectance with the control one. In addition, HMF could enhance the rutting resistance of asphalt mastic according to the results of multiple stress creep recovery tests. When HMF replaced LMF, the thermal conductivity of the asphalt mixture with HMF increased by 0.26 W/(m·K) (the reference value was 1.72 W/(m·K)). The combined effect of high emissivity and thermal conductivity led to a lower surface temperature (i.e., -5.4 °C) in the tests. The results of this study demonstrate that HMF is a potential material to cool asphalt pavements.

Economic Evaluation of Cement Grouted Bituminous Mixes for Airport Pavements.

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.

Effect of Sampietrini Pavers on Urban Heat Islands.

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.

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.