Pavement Distress Detection Methods: A Review

Abstract

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.

Sustainable Road Maintenance Planning in Developing Countries Based on Pavement Management Systems: Case Study in Baja California, México

“…It is established that the first input data should reflect pavement conditions regarding surface and structure. The surface analysis refers to an evaluation of surface faults through a pavement distress survey [30], as well as the measurement of two performance indicators, which are the International Roughness Index (IRI) and the Rut Depth (RD). For its part, the structural analysis refers to assessing the structure of the pavement, considering the measurement of thicknesses, quality of the materials that make it up and load capacity.…”

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

“…The critical and novel step in this process was the development of the distress categories for the model. Typically, pavement distresses can be broken down into four major categories namely: cracking, visco-plastic deformation, surface defects and other miscellaneous types [13]. The grouping is shown in Table 1.…”

“…The grouping is shown in Table 1. Of these categories, other studies have shown that there is a direct relationship between the impact each distress has upon safety and comfort per level of severity [13]. Based on this each distress has an impact based on the severity of the occurring distress.…”

“…The laser profiling systems have been developed based on this and are seen to be components in several commercial road systems such as ARAN, ROMDAS and Dynatest [5] and they offer accurate assessments of the state of the road but at a higher cost. With respect to the image-based systems, there has been a lot of research done on the use of images to detect and analyze specific distresses with systems being developed around Digital cameras, Line-scan cameras and 3D Imaging with methods achieving suitable accuracies in replicating distress patterns and classifying them [13]. These systems offer a cheaper alternative to carrying out distress surveys as opposed to laser-based systems.Recent work has also included research on low-cost assessments of 3D models for pavement distress analysis wherein the accuracy of models created using the techniques of photogrammetry and 3D modelling have been validated [14].…”

Remote Sensing Technologies for Linear Infrastructure Monitoring

“…In Highway and especially in airport the evaluation of road pavement performance is essential for effective maintenance design engineering (Barbarella et al, 2018;Barbarella et al, 2017). The research is oriented towards the study of survey methods in order to obtain increased safety, higher data collection speed, higher accuracy data, and minimising traffic interference (Ragnoli et al, 2018;Yen et al, 2011). The 3D laser scanner technology has high potential to collect reliable road profiles, moreover it is possible to visualize the pavement roughness covering the entire pavement (Chang et al, 2006).…”