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The accumulative freeze-thaw cycles in cold regions could cause deteriorations to concrete pavements. The measurement of air-void system inside hardened concrete is important for estimating the freeze-thawing performance of a concrete pavement. The segmentation of air voids from the concrete surface image is one of the most essential and challenging steps for automated air-void measurement. Conventional automated air-voids segmentation methods rely on a color enhancement step to manually create a contrast between air voids and non-air-voids. This study aims to investigate the use of a photometric stereo method for automated segmentation of air voids in hardened concrete surface without a need for contrast enhancement. In the study, the photometric stereo system with a Charge-Coupled Device (CCD) camera and six illumination lights was designed to collect images of polished concrete specimen surfaces under various lighting directions. Two air-void segmentation approaches were developed in the study. One approach interpreted the air voids by the surface depth. The other approach interpreted the air voids by the surface normal. Consequently, the segmentation results were refined by a set of image processing procedures. The results showed that the proposed automated air-void segmentation procedures could detect air voids in concrete surface with a satisfactory accuracy. [ABSTRACT FROM AUTHOR]

Data quality assurance (QA) procedures have been developed and implemented to evaluate the quality of automated pavement distress data. Most State Departments of Transportation (DOTs) use fixed acceptance thresholds to evaluate data quality at the network level and project level. However, errors of automated measurements can be affected by many factors such as pavement types and pavement conditions. It is necessary to take those factors into account while developing the QA acceptance thresholds. The objective of this research is to propose thresholding methods that could effectively utilise these factors to improve QA of the automated data. In this study, the potential problems associated with the use of fixed acceptance thresholds for QA were first discussed. Then, one conventional prediction interval method and one bootstrapping prediction interval method were proposed for QA procedures. The proposed prediction interval based methods can be used to determine variable thresholds that are sensitive to measurement errors. Lastly, QA results from the proposed methods and a fixed thresholding method were compared and analysed. The experimental results showed that prediction interval methods were superior to the fixed threshold methods in identifying pavement sections with data quality issues and evaluating the overall data quality of the network. [ABSTRACT FROM AUTHOR]

Data quality assurance (QA) procedures have been developed and implemented to evaluate the quality of automated pavement distress data. Most State Departments of Transportation (DOTs) use fixed acceptance thresholds to evaluate data quality at the network level and project level. However, errors of automated measurements can be affected by many factors such as pavement types and pavement conditions. It is necessary to take those factors into account while developing the QA acceptance thresholds. The objective of this research is to propose thresholding methods that could effectively utilise these factors to improve QA of the automated data. In this study, the potential problems associated with the use of fixed acceptance thresholds for QA were first discussed. Then, one conventional prediction interval method and one bootstrapping prediction interval method were proposed for QA procedures. The proposed prediction interval based methods can be used to determine variable thresholds that are sensitive to measurement errors. Lastly, QA results from the proposed methods and a fixed thresholding method were compared and analysed. The experimental results showed that prediction interval methods were superior to the fixed threshold methods in identifying pavement sections with data quality issues and evaluating the overall data quality of the network. [ABSTRACT FROM AUTHOR]