Variability of Film-Derived Pavement Distress Data

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.

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

“…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

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.

“…The frequency of distress data collection further adds to the uncertainty in performance prediction. The uncertainty associated with the method (manual, semiautomated, and automated) of condition data collection has been well documented in the literature (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). However, there is a need to investigate the effects of condition monitoring frequency on pavement management decision making.…”

mentioning

confidence: 99%

Effect of Frequency of Pavement Condition Data Collection on Performance Prediction

Haider

Baladi

Chatti

et al. 2010

Monitoring pavement surface conditions over time is essential for pavement management and performance models. Time series distress data can be used to determine the remaining service life at the project level, which can then be used for all projects to assess the overall health of the pavement network. Observed field performance is also crucial for calibrating performance prediction models for pavement design purposes. Therefore, highway agencies collect pavement condition data to accomplish both policy and engineering objectives. However, differences exist among agencies between the monitoring frequency used for pavement surface distress (imaging) and that used for sensor-measured features. These differences pertain primarily to the relative difficulties in collecting and processing imaging data. Many agencies collect sensor data more frequently than images. Most highway agencies monitor pavement condition at 1-, 2-, or 3-year frequencies. Discrepancies between performance model predictions and observed field performance are conventionally attributed solely to errors in predicted pavement distresses. In fact, inherent uncertainty may also be present in measured pavement distresses due to spatial variability, sampling, and measurement errors. The frequency of distress data collection adds further uncertainty in performance prediction. This paper explores the effect of pavement condition monitoring frequency on pavement performance prediction. Analyses of observed pavement performance show that condition data collection frequency can significantly affect performance prediction. Therefore, more frequent data collection for image-based methods can reduce the associated risk in performance prediction and thus be more effective for better decision making for pavement management.