Surface Distress Data Research Articles

Analysis and Prediction of Pothole Formation Rate Using Spatial Density Measurements and Pavement Condition Indicators

Potholes represent one of the dangerous distress types on roads. They form as a result of various factors, including water ingress, freeze–thaw, and pavement condition deterioration. In the UK, 1.7 million potholes were repaired in 2021; this critical number causes significant economic, social, and environmental impacts; there is no tool able to predict the number of potholes that might appear in a road network. This study aims to analyze pothole formation and its relationship with other distress types and their severity, and to develop a simple tool able to predict the number of potholes that might appear in a road network based on the network condition. Significant pothole data from the road network of Greater London between 2017 and 2020, in addition to surface distress data, were used in this study. ‘Spatial density’ and ‘join’ tools embedded in ArcGIS were used to correlate pothole spatial density (PSD) with the road condition surrounding the potholes. This analysis was then used to calculate PSD as a function of different condition indicators, such as road condition index and crack intensity, allowing prediction of the number of potholes based on the length and condition of the sections being analyzed. The results demonstrate that potholes are significantly concentrated in sections with deteriorated conditions. They also show that it is possible to predict the number of potholes using PSD with reasonable accuracy. Lastly, it was found that sections with low crossfall are more susceptible to pothole formation, presumably because of water ponding and consequent damage.

Subsurface Condition Evaluation of Asphalt Pavement for Pavement Preservation Treatments

Abstract The Indiana Department of Transportation (INDOT) has developed a guideline for evaluation of subsurface condition, a project-level evaluation for applicability of Indiana pavement preservation treatments (PPT). The developed guideline, using ground penetration radar (GPR) measurements, surface distress, and laboratory tests, determines the pavement subsurface distress severity and its distribution. The guideline also incorporates the newly developed water stripping severity test utilizing the digital image processing. The guideline has hierarchal evaluation system based on the type of data available for the test section. Level 1 is selected if GPR analysis data is available, level 2 is selected if surface distress data is available, and level 3 is used for all other cases. In addition, the subsurface distress distribution analysis tool (DCUAL) provides the locations of the PPT applicable sections. Case studies were conducted to provide aid to better understand the guideline, to present the example evaluation results upon the application of the guideline, and to validate the applicability of guidelines. Three test roads treated with PPTs within two to three years in Indiana were selected, and different evaluation levels were applied to the test roads. Level 1 and level 2 were applied for state road (SR) SR-70, and level 2 and 3 was applied for SR-257 and SR-43, respectively. Level 1 and level 2 analyses in the case study showed a good agreement with the case of the non-uniform subsurface distress distribution. In the process of determining the PPT applicability, pavements with the overall score of 60 or higher were found to be suitable for PPTs for all three levels of analysis and suitable for the Indiana State Roads having average annual daily traffic of less than 9000. Overall, the case study validated that the guideline provides a consistent, rational, and data-driven decision-making process for the applicability of the project-level pavement preservation program.

Extracting Pavement Surface Distress Conditions Based on High Spatial Resolution Multispectral Digital Aerial Photography

AbstractState transportation agencies regularly collect data on pavement surface distresses. These data are used to assess overall pavement conditions and to make maintenance and repair decisions. Routinely-acquired and publically-available high spatial resolution (hsr) multispectral digital aerial photography provides a potential method for collecting distress information that can supplement or replace currently-used technologies. Principal component analysis and linear least squares regression models were used to evaluate the potential of using HSR multispectral digital aerial photographs to estimate pavement surface overall distress conditions. Various models were developed using HSR multispectral digital aerial photographs of different spatial resolution (6-inch, 12-inch, and 24-inch) and reference pavement surface distress data collected manually at multiple sample sites using standard protocols. The results show that the spectral response of HSR multispectral digital aerial photographs correlate strongly with reference distress rates at all tested spatial resolutions, but the 6-inch aerial photos exhibit the strongest correlation (R2 > 0.95), even when using only half of the sample sites (R2 > 0.92). These results indicate that straightforward analysis of HSR multispectral digital aerial photographs, routinely acquired by most municipalities and states, can permit assessment of pavement surface distress conditions as well as current manual evaluation protocols.

Utilizing Asphaltic Concrete Overlay to Mitigate the Detrimental Effects of Alkali Carbonate Reactions in Portland cement Concrete Pavement

In 1990, the Louisiana Department of Transportation and Development (LADOTD) constructed approximately 9.96 km of portland cement concrete (PCC) pavement on Interstate 20. In 1994, the PCC began to show signs of distress in the form of surface cracks, joint spalling, pop outs, and efflorescence. Alkali carbonate reactions (ACR) in the PCC were discovered upon analyzing cores taken from the PCC in 1996. In 1997 and 1998, LADOTD placed a 102-mm AC overlay on the PCC pavement on the east- and west-bound lanes, respectively. Sawing and sealing the AC over the PCC joints was performed to mitigate reflective cracking. The AC overlay added structure to the pavement section reducing stress on the PCC, created a barrier preventing surface moisture from entering into the PCC, and created a thermally insulated pavement reducing the temperature within the PCC. Pavement surface distress data was obtained by data mining the LADOTD Pavement Management system data base and structural testing the composite pavement was conducted with the Falling Weight Deflectometer. Cores were obtained from the PCC and tested in accordance with a modified ASTM C1567-13 in October 2012. Approximately 15 years after the placement of the AC overlay, only minimal transverse cracking, longitudinal cracking, and patching were discovered and the roadway had a smooth ride. Results from the structural testing indicated that the PCC pavement was distressed with the overall structural number of the pavement being approximately 5.5. When tested in accordance with a modified ASTM C1567-13, significant volumetric strains up to 0.8 percent were measured on the PCC cores. Volumetric strains of that magnitude will cause detrimental effects on the PCC, eventually leading to its destruction.

Methodology to Evaluate Quality of Pavement Surface Distress Data Collected by Automated Techniques

Highway agencies commonly use automated pavement data collection techniques to collect pavement surface distress data at the network level. Although an immense amount of data is collected at the network level, agencies realize that there is a lack of understanding of the quality of the data collected. Traditionally, either the overall pavement condition rating or individual distress ratings are used to evaluate the quality of the condition data. However, each measure has its own pros and cons, rendering the use of a single measure inadequate. This paper presents a set of performance measures that highway agencies can use to quantify the quality of the pavement condition data collected and processed. The set of measures consists of ( a) the pavement condition rating and hypothesis testing for differences, ( b) the percentage cumulative difference in the pavement condition rating over its entire range, and ( c) kappa statistics for individual distresses. This set of performance measures can be used to assess the effectiveness of an automated method for the collection of pavement condition data and the effect of sampling on the pavement condition ratings obtained by automated techniques. The effectiveness of an automated technique is assessed by comparison with the findings of benchmark manual visual surveys. The performance measures offer a complete assessment of the effect of sampling on the overall pavement condition rating, its variation over the entire range, and the identification of individual surface distresses.

Cohen’s Weighted Kappa Statistic in Quality Control–Quality Assurance Procedures: Application to Network-Level Contract Pavement Surface Condition Surveys in British Columbia, Canada

Like many provincial and municipal agencies, the British Columbia Ministry of Transportation (BCMoT) contracts out the collection of pavement surface condition data. Because BCMoT is committed to contracts with multiple private contractors, quality assurance (QA) plays a critical role in ensuring that the data are collected accurately and repeatably from year to year. Comprehensive QA testing procedures for surface distress data have been developed and implemented since the data collection has been based on visual ratings with event boards. Control sites that are manually surveyed are used to evaluate whether the contractor is correctly applying the BCMoT pavement surface distress rating system. To date, the QA testing has been based on a composite-index–based criterion for assessing the level of agreement and supplemented with the detailed severity and density rating data. However, the use of a composite index presents some limitations related to the model formulation and weightings assigned to particular distress types. Although the detailed ratings are useful as a diagnostic tool to pinpoint discrepancies, in the disaggregated format, they are not conducive as acceptance criteria for QA testing. Not widely used in the field of engineering, Cohen’s weighted kappa statistic has been applied since the 1960s in other areas to assess the level of agreement beyond chance among raters. The statistic was therefore identified as a possible solution for improving the ministry’s QA surface distress testing process by providing an overall measure of the level of agreement between the detailed manual benchmark survey and the contractor severity and density ratings. The application is described of Cohen’s weighted kappa statistic for visual surface distress survey QA testing using the BCMoT survey and testing procedures as a case study.

Estimating International Roughness Index from Pavement Distresses to Calculate Vehicle Operating Costs for the San Francisco Bay Area

Incorporating models that estimate road user costs and user benefits or both from different management strategies would increase the road network asset management system capabilities. Vehicle operating cost (VOC) can be a major part of road user costs, and pavement surface roughness is a major factor that affects VOC. This study was based on an effort to establish a correlation between pavement roughness and pavement distresses for asphalt concrete surface with the objective that the developed roughness model could be utilized to estimate VOCs for the streets in the cities and counties of the San Francisco Bay Area directly from pavement distresses. Due to the lack of roughness data for Bay Area city streets, Strategic Highway Research Program distress data for California Long-Term Pavement Performance sites were used as an initial effort to establish the model. A systematic approach was used to come up with a reasonable correlation between international roughness index (IRI) and pavement condition. The current roughness model can be used to estimate surface roughness if the pavement surface distress data are available. This model could be included, along with an additional incorporation of established relations between VOC and IRI, in the Bay Area agencies' pavement management system, to estimate VOC directly from pavement distresses. Finally, some recommendations are provided to the agencies in the Bay Area cities and counties on how to utilize the model and how to refine the initially established model in the future.

Designs and Implementations of Automated Systems for Pavement Surface Distress Survey

The categorization and quantification of the type, severity, and extent of surface distress is a primary method for assessing the condition of highway pavements. Various methodologies were developed to automate pavement distress surveys. Significant technical advances were made during the past years. The implementations of the image capturing subsystems include conventional analog-based area-scan, analog and digital line-scan, laser scanning, and shadow Moire method. Newer implementations of image processing include artificial neural net and parallel processing. However, problems still exist in the areas of implementation costs, processing speed, repeatability, and accuracy. This paper presents some of the key developments in recent years for automating pavement distress evaluation. These new systems are described in terms of their potential and applicability. This paper also presents a modified approach to collecting and processing surface distress through the use of high-performance digital cameras for the acquisition of surface distress data.

Developing a Quantitative Rating System for Continuously Reinforced Concrete Pavement

A jointed concrete pavement rating system based on sampling and quantitative surface distress data was developed several years ago for North Carolina. This rating system has been adopted by the pavement management system for the priority ranking of projects. The first attempt to develop a similar rating system for continuously reinforced concrete (CRC) pavement was made with no fruitful results. There remains an urgent need for a comparable CRC rating system for project ranking. Another attempt that used a different approach was made. A linear regression equation was developed, and the correlation with experts’ rating was reasonably good.

“Off-the-Wall” Pavement Distress Variability Study

Variability of pavement surface distress data collection has always been an area of significant concern. When conducting evaluations of distress data manually (with raters observing pavements in question, interpreting what they see, and recording on paper) the process is subject to human errors. To minimize the impact of such human errors on these important pavement performance data, sophisticated equipment has been developed to eliminate as much of the human intervention as possible. Such technology is not without its own limitations of precision and bias. With both methodologies being used for the collection of surface distress data for the long-term pavement performance (LTPP) program, questions regarding precision and bias have been identified. In attempting to define the variability of the data for incorporation in stochastic analyses, it has become apparent how diverse and complex these distress data truly are. To adequately quantify the precision and bias, a detailed experiment was designed to evaluate the errors inherent in the different distress data collection methodologies. The facet of the experiment reported targets the variability of human distress surveyors and the biases associated with conducting surveys from film, using a slightly different projection system. Specifically, a collection of surveyors was assembled to establish the variability associated with experienced raters versus novice raters, engineers versus engineering technicians, and teams versus individuals.

Digital image processing as a tool for pavement distress evaluation

The information obtained through accurate condition assessment of pavement surface distress data is needed as an essential input to any decision making process concerning pavement management policy. At the same time technological advances in automated inspection systems provide the opportunity to automate the collection and evaluation of the pavement surface condition. In this paper a method developed jointly by the Laboratories of Highway Engineering and Photogrammetry of the National Technical University of Athens is described and proposed. The method involves digital image processing techniques to provide suitable digital imagery as input to specialised software developed especially for this project. This software determines objectively and fully automatically the type, the extent and the severity of surface crackings for flexible road pavements. The proposed method presenten substantial agreement, when compared with systematic visual ratings of existing pavement crackings carried out according to the internationally accepted requirements for airfield and road pavement of the Federal Aviation Administration (FAA).