Laboratory Investigation of Fiber Bragg Grating Strain Sensors for Semirigid Base Asphalt Pavements

Aiming to evaluate the applicability of Fiber Bragg Grating (FBG) strain sensors for semirigid pavement monitoring, beam and cylinder specimens with three types of FBG strain sensors embedded in two kinds of classic semirigid pavement materials, asphalt mixture (AC-25) and cement-stabilized crushed stones (CSCS), were prepared in the laboratory. Four-point bending tests and uniaxial-compression tests were carried out under different loading frequencies and temperatures to evaluate the working properties of these sensors and then obtain the corresponding real sensitivity coefficients (SCs). The experimental results showed that the synchronism, repeatability, and linearity of all these sensors were prominent. However, the real SC results were significantly different from the recommended and dependent on many factors including temperature, the loading frequencies, the stress state, and the type of embedded material to different degrees. The SCs remained stable when the moduli of the embedded materials were high enough; otherwise, the SCs varied. Two SC prediction models that used the modulus of the embedded material as the only independent variable were developed to deal with the problem of instability. The modulus difference level between the sensors and the embedded material could integrate the factors roughly, except for the stress state. It is recommended that the factors above should be considered when using FBG strain sensors in practice, and it is still necessary to perform laboratory calibration in advance.

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects.

Pavement Distress and Debris Detection using a Mobile Mapping System with 2D Profiler LiDAR

Regular pavement monitoring over highways and airport runways is vital for public agencies to ensure the safe riding of vehicles and aircrafts. Highways are mostly subject to cracking and potholes along with a few instances of debris around construction work zones. Airports are also concerned with debris but have much lower tolerance for the presence of foreign object debris (FOD) that could possibly damage the aircraft. LiDAR is rapidly emerging in a variety of mobile mapping systems (MMS) and will likely be integrated into many transportation vehicles over the next decade for pavement inspection. This paper proposes a unique algorithm for pavement surface inspection with the help of MMS driven at highway speeds. The study analyzed LiDAR data acquired for 8 mi of highway collected at approximately 55 to 60 mph. This study indicates that an adequately designed MMS along with the proposed algorithm can efficiently detect pavement anomalies as small as 2 cm in the form of cracking, potholes, surface debris, or any combination of these. This is more than sufficient for highways, where debris such as ladders and tires are an order of magnitude larger. For evaluating the effectiveness of detecting smaller airport FOD, a validation dataset was created by driving the MMS at 15 mph adjacent to a debris field of 50 sample pieces of FOD collected from an airport. The study found that 100% of the FOD items larger than 2 cm in size (12 out of 50 samples) were detected successfully at 15 mph. Both datasets suggest that MMS LiDAR is sufficient for pavement inspection and as sensor fidelity increases, even small FOD will be able to be detected with the algorithm proposed in this paper.

An innovative approach for high-performance road pavement monitoring using black box

One of the criteria adopted by the Word Bank with the aim of defining the economic level of a country is represented by the condition of the road pavements. To ensure adequate road pavement quality, road authorities should be continuously monitoring and repair the detected anomalies. To fast solve problems associated with poor quality of road surface such as comfort or safety, the presence of distress must be detected quickly. The high-performance pavement distress detection, such as those base on the image processing or on the laser scanning, is very expensive and does not allow to the road administration to conduct the appropriate monitoring campaigns. To solve these problems, the paper describes the pave box methodology, an innovative and immediately operational distress detection approach based on the exploitation of data collected by the black boxes located inside the vehicles that routinely pass on the road network. Data processing and the algorithms used in the post-processing evaluation of the vertical acceleration were compared with existing visual surveys procedures such as PCI. Two different indices have been proposed to detect and classify both the local damages and the global condition of the entire road. Pave box provides a robust evaluation of the pavement condition that allows to detect all the severe distress and not less than 70% of the minor damages on the pavement surface. The proposal is characterized by low time and cost consumption and it represents an effective tool for road authorities.

An Architectural Multi-Agent System for a Pavement Monitoring System with Pothole Recognition in UAV Images

In recent years, maintenance work on public transport routes has drastically decreased in many countries due to difficult economic situations. The various studies that have been conducted by groups of drivers and groups related to road safety concluded that accidents are increasing due to the poor conditions of road surfaces, even affecting the condition of vehicles through costly breakdowns. Currently, the processes of detecting any type of damage to a road are carried out manually or are based on the use of a road vehicle, which incurs a high labor cost. To solve this problem, many research centers are investigating image processing techniques to identify poor-condition road areas using deep learning algorithms. The main objective of this work is to design of a distributed platform that allows the detection of damage to transport routes using drones and to provide the results of the most important classifiers. A case study is presented using a multi-agent system based on PANGEA that coordinates the different parts of the architecture using techniques based on ubiquitous computing. The results obtained by means of the customization of the You Only Look Once (YOLO) v4 classifier are promising, reaching an accuracy of more than 95%. The images used have been published in a dataset for use by the scientific community.