Distress Data Collection Research Articles

Update of Long-Term Pavement Performance Manual Distress Data Variability: Bias and Precision

Pavement distress surveys based upon field interpretation and manual mapping and recording of the distress information on paper forms has been used in the Long-Term Pavement Performance (LTPP) program to collect important pavement condition and distress data. Although this manual method was used in the past as a backup to the 35-mm black and white photographic-based method, recently the use of manual distress survey methods has increased in intensity and coverage. To promote uniformity and consistency of distress data collection, one of the early LTPP efforts was to develop standard definitions, measurement procedures and data collection forms. Various quality control and quality assurance functions have also been implemented to provide for high quality data. However, despite these efforts, manual surveys are still based upon a single rater’s subjective classification of distresses present in the field. Recognizing that rater variability exists, a study was undertaken by FHWA to assess the level of variability between individual distress raters and to address the potential precision and bias. Results from nine LTPP distress rater-accreditation workshops conducted during the period of 1992 to 1996 were used as the source of data. Analyses of those data led to numerous observations and conclusions regarding the bias and precision of LTPP distress data. Because LTPP distress data are to be used in the development of pavement performance prediction models, it is believed that the level of variability found in this study should be reduced to increase its potential usage in the development of such models. A number of recommendations to improve the variability associated with manual distress surveys data are included.

Contracting for Pavement Distress Data Collection

Many agencies responsible for managing pavements have adopted pavement management systems (PMS) to help manage their pavement networks more cost-effectively. One of the most costly parts of operating a PMS is collecting condition information, especially pavement distress information. Many agencies have started to contract for pavement distress data collection. Some of the agencies have experienced problems with the data collected by contract. A study for agencies in Washington and Oregon to define the accuracy of data needed by the agencies with an evaluation of certain participating vendors using semiautomated data collection methods is described. Issues about quality control and quality assurance faced by agencies considering contracting for automated data collection also are raised. These issues need additional study to develop appropriate guidelines. The initial set provided is based on discussions with some of the agencies currently contracting for pavement distress data collection.

Assessment of Long-Term Pavement Performance Program Manual Distress Data Variability: Bias and Precision

The use of manual survey methods within the Long-Term Pavement Performance (LTPP) program for the collection of distress data has drastically increased both in intensity and in coverage over the past couple of years. Because these surveys are conducted by individual raters whose biases can lead to variability between raters, it was hypothesized that distress data variability existed and that it could potentially be quite large. Thus, the purpose of the presented study was to quantify manual distress data variability, with special emphasis on the bias and precision of the data. Results from seven LTPP program distress rater accreditation workshops conducted during the period from 1992 to 1995 were used as the only source of data. On the basis of analyses of these data, both the apparent bias and the precision for the common distress type-severity level combinations were quantified. It was also concluded from this study that individual rater variability for any given distress type-severity level combination is typically large and increases as the distress quantity increases; however, when all distress type-severity level combinations are viewed in terms of a single composite number such as the pavement condition index value, there is excellent agreement between the individual raters, the group mean, and the ground truth value, and individual rater variability is also quite small. Because LTPP program distress data are to be used in the development of pavement performance prediction models, improvements in variability are highly desirable to ensure that they serve their intended purpose. Recognizing that the LTPP program distress raters are experienced individuals, such improvements are not envisioned to come through additional training. It is the authors’ contention that the only way of achieving the desired improvement is through the conduct of group consensus surveys.

Analysis of Segmentation Algorithms for Pavement Distress Images

Collection and analysis of pavement distress data is an important component of any pavement-management system. Various systems are currently under development that automate this process. They consist of appropriate hardware for the acquisition of pavement distress images and, in some cases, software for the analysis of the collected data. An important step in the automatic interpretation of images is segmentation, the process of extracting the objects of interest (distresses) from the background. We examine algorithms for segmenting pavement images and evaluate their effectiveness in separating the distresses from the background. The methods examined include the Otsu method, Kittler’s method, a modified relaxation method, and a method is based on a threshold estimated by regression analysis. Comparison of the algorithms on a data set of asphalt pavement images indicates that the relaxation and regression thresholding methods consistently outperform the other methods. The regression thresholding method has the potential to become the method of choice due to its computational advantage over the relaxation method.

Primitive‐Based Classification of Pavement Cracking Images

Collection and analysis of pavement distress data are receiving attention for their potential to improve the quality of information on pavement condition. We present an approach for the automated classificaton of asphalt pavement distresses recorded on video or photographic film. Based on a model that describes the statistical properties of pavement images, we develop algorithms for image enhancement, segmentation, and distress classification. Image enhancement is based on subtraction of an “average” background: segmentation assigns one of four possible values to pixels based on their likelihood of belonging to the object. The classification approach proceeds in two steps: in the first step, the presence of primitives (building blocks of the various distresses) is identified, and in the second step, classification of images to a distress type (using the results from the first step) takes place. The system addresses the following distress types: longitudinal, transverse, block, alligator cracking, and plai...

Digital Imaging Concepts and Applications in Pavement Management

The potential application of image-processing methods to automate the manual data collection and visual rating of pavement surface conditions has not been widely recognized until relatively recently. A cost-effective automated system to capture and extract pavement-surface distress from video images would improve safety and efficiency, and could offer consistency and uniformity of data and data quality, both locally and nationally. The purpose of this paper is to provide an introduction for transportation engineers, especially pavement engineers, to digital image-processing concepts and applications in pavement management. This includes pavement-surface-distress data of concern, basic machine-vision and digital-image-processing concepts, video-system characteristics for automated distress-data collection, and existing systems for digital imaging in pavement management in the United States, Japan, and France. It is concluded that in a relatively short time substantial progress has been made in the development of automated systems for distress-data acquisition and interpretation, and enhanced capabilities can be expected in the near future. Digital imaging technology is playing a significant role in these efforts.