Assessment of Long-Term Pavement Performance Plan Wall Projection-Based Distress Data Variability

Shekharan, A R; Rada, Gonzalo R.; Elkins, G E; Bellinger, William Y.

doi:10.3141/1643-13

Cited by 4 publications

(4 citation statements)

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“…Recognizing that the LTPP program distress raters are experienced individuals, such improvements are not envisioned to come through additional training and they contend that the only way to achieve the desired improvement is to conduct group consensus surveys. Shekharan et al (1998) also showed that the overall variability of manual distress data is lower than for that from film interpretations. Furthermore, the bias (average difference between manual and film interpretations on the same sections) was much higher for the film interpretation than for the manual surveys.…”

Section: Quality Management In Automated Pavement Data Collectionmentioning

confidence: 93%

Automated Pavement Condition Data Collection Quality Control, Quality Assurance, and Reliability

Ong¹,

Noureldin²,

Sinha³

2011

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In recent years, state highway agencies have come to understand the need for high quality pavement condition data at both the project and network levels. At the same time, agencies also realize that they have become too dependent on contractors to ensure the quality of the delivered data without any means to independently assure the quality of these delivered data. This research study therefore aims to investigate the inherent variability of the automated data collection processes and proposes guidelines for an automated data collection quality management program in Indiana. In particular, pavement roughness data (in terms of IRI) and pavement surface distress data (in terms of PCR and individual pavement surface distress ratings) are considered in this study. Quality control protocols adopted by the contractor are reviewed and compared against industry standards. A complete quality control plan is recommended to be adopted for all phases of the data collection cycle: preproject phase, data collection phase, and post-processing phase. Quality assurance of pavement condition data can be viewed in terms of (i) completeness of the delivered data for pavement management; (ii) accuracy, precision and reliability of pavement roughness data; and (iii) accuracy, precision and reliability of individual distress ratings and an aggregate pavement condition rating. An innovative two-stage approach is developed in this study to evaluate delivered data for integrity and completeness. Different techniques and performance measures that can be used to evaluate pavement roughness and pavement surface distress data quality are investigated. Causes for loss in IRI and PCR accuracy and precision are identified and statistical models are developed to relate project-and network-level IRIs and PCRs. Quality assurance procedures are then developed to allow highway agencies improve their pavement condition data collection practices and enhance applications in the pavement management systems.

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Section: Quality Management In Automated Pavement Data Collectionmentioning

confidence: 93%

Automated Pavement Condition Data Collection Quality Control, Quality Assurance, and Reliability

Ong¹,

Noureldin²,

Sinha³

2011

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“…• LTPP Distress Variability Study (4,(7)(8)(9)(10). The objectives of this study were to assess the variability of manual and film-derived distress data collected for the LTPP program and to quantify the bias and precision of those distress data.…”

Section: Other Qc-qa Elementsmentioning

confidence: 99%

Collecting and Interpreting Long-Term Pavement Performance Photographic Distress Data: Quality Control-Quality Assurance Processes

Rada¹,

Simpson²,

Hunt³

2004

Transportation Research Record

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The Long-Term Pavement Performance (LTPP) program is making use of photographic technology to provide, first, detailed, distress-specific condition data for use in the development and validation of pavement performance models and, second, a permanent, objective, high-resolution record of pavement condition over the full length and width of the sections in the program. Because high-quality distress data are critical to the success of the LTPP program, numerous quality control-quality assurance (QC-QA) processes have been implemented over the life of the program. Those processes address data quality before the start of data collection, during data collection, during data interpretation, after data interpretation, and after the uploading of data to the LTPP database. Some of the processes are a direct result of advance planning, based on past experience, while others are the result of lessons learned in the course of the program. Some were implemented early in the program, while others were introduced well into the program. The Distress Identification Manual, distress rater accreditation workshops, time series review, database checks, data studies and analyses, and feedback reports are just a few of the elements that make up the full suite of QC-QA processes. A detailed summary of the QC-QA processes associated with the LTPP photographic distress data is presented.

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“…Thus, the frequency of distress data collection could add to the uncertainty in performance prediction (2). Uncertainty associated with the method of condition data collection (manual, semiautomated, and automated) has been well documented in the literature (ASTM STP 1121; [3][4][5][6][7][8][9][10][11][12][13][14]. However, an investigation of the effects of condition monitoring frequency on pavement management decision on highway maintenance, rehabilitation, and reconstruction is needed.…”

mentioning

confidence: 99%

Impact of Pavement Monitoring Frequency on Pavement Management System Decisions

Haider

Chatti

Baladi

et al. 2011

Transportation Research Record

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Pavement performance monitoring is an essential part of a pavement management system (PMS). Therefore, highway agencies collect pavement condition data containing various structural and functional distresses on a regular basis. How frequently pavement condition data are collected varies. The effect of pavement monitoring frequency on pavement performance prediction and its consequence at network-level PMS decisions were explored. A statistical methodology was developed to investigate the impact of monitoring frequency on performance prediction with different model forms. The results of the analyses showed that performance predictions were affected by monitoring interval. Furthermore, the various types of distress to be collected might need different monitoring intervals because of their unique growth over time and associated uncertainty in prediction. When cost consequences and prediction uncertainties were combined, monitoring of cracking (image based) at 1-year intervals appeared to be more appropriate, whereas for roughness (sensor based), a monitoring interval of 1 to 2 years appeared to be suitable. The results of network-level analyses demonstrated that monitoring interval might significantly affect the short- and long-term network conditions for various preservation strategies. Increasing the monitoring interval may have consequences for PMS decisions. Longer intervals for crack monitoring (image based) might cause an overestimation of agency costs for pavement repair at the network level. Longer intervals for international roughness index monitoring (sensor based) will result in an underestimation of repair costs at the network level.

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Assessment of Long-Term Pavement Performance Plan Wall Projection-Based Distress Data Variability

Cited by 4 publications

References 0 publications

Automated Pavement Condition Data Collection Quality Control, Quality Assurance, and Reliability

Automated Pavement Condition Data Collection Quality Control, Quality Assurance, and Reliability

Collecting and Interpreting Long-Term Pavement Performance Photographic Distress Data: Quality Control-Quality Assurance Processes

Impact of Pavement Monitoring Frequency on Pavement Management System Decisions

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