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

1998 ◽  
Vol 1643 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Gonzalo R. Rada ◽  
Chung L. Wu ◽  
Gary E. Elkins ◽  
Rajesh K. Bhandari ◽  
William Y. Bellinger
Keyword(s):  
Data Collection ◽  
Prediction Models ◽  
Survey Methods ◽  
Quality Data ◽  
Pavement Performance ◽  
Pavement Condition ◽  
Pavement Distress ◽  
Black And White ◽  
Pavement Performance Prediction

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.

A mechanistic evaluation of modified asphalt paving mixtures

1994 ◽  
Vol 21 (6) ◽  
pp. 954-965 ◽  
Author(s):  
N. Ali ◽  
Shaher Zahran ◽  
Jim Trogdon ◽  
Art Bergan
Keyword(s):  
Prediction Model ◽  
Pavement Performance ◽  
Pavement Distress ◽  
Modified Asphalt ◽  
Significant Difference ◽  
Actual Field ◽  
Asphalt Paving ◽  
Polymer Modified Asphalt ◽  
Pavement Performance Prediction

The main purpose of this study was to facilitate decisions concerning the effectiveness of modifiers in mitigating pavement distress and improving long-term overall pavement performance in actual field conditions, by utilizing short-term laboratory results and a mathematical prediction model. The modifiers investigated were carbon black, neoprene latex, and polymer modified asphalt (STYRELF). The statistical general linear model (GLM) and the Fisher least significant difference (LSD) were used for the analysis of data. The results of the study indicate that the effect of the modifier on the paving mixture properties was insignificant at low temperatures (down to −17 °C), but significant at high temperatures (up to 60 °C) where the synergistic effect of the modifier on the paving mixture was pronounced. The VESYS IIIA pavement performance prediction model was utilized to assess the effects, if any, of the modifier on the pavement's overall performance. All the modifiers improve, to some degree, the overall pavement performance. Key words: modifiers, asphalt, paving mixtures, pavements, polymer asphalt.

scholarly journals Flexible Pavement Performance Evaluation in Ontario: an Overview

2021 ◽  
Author(s):  
Muzaffar Hassan
Keyword(s):  
Management System ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Ride Quality ◽  
Physical Parameters ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Pavement Distress ◽  
Verification Techniques

Measuring pavement performance is a major component of the pavement management system. It assists in decision-making for finding the optimum strategies to provide, evaluate, and maintain serviceability in an acceptable condition cost effectively. The Ontario Ministry of Transportation (MTO) has been systematically rating pavement performance since the mid-1960s. Pavement condition survey involves measurement of two physical parameters: ride quality of pavement surfaces, and the extent and severity of pavement distress manifestations. The pavement ride quality can be measured with an acceptable level of consistency and repeatability through automation. However, achieving consistency in the evaluation of pavement distress manifestations is a challenging task because the automation that could accurately and consistently detect, quantify and record surface distresses is not fully developed is spite of rapid advances in video imagery and non-contact sensing devices. This report evaluates the progress made over the past three decades in the key areas of Distress Manifestation Index, Riding Comfort Rating, Pavement Condition Index and second generation Pavement Management System (PMS2). A review of the Ministryʼs network-level pavement performance database is presented, emphasizing pavement condition surveys, prediction models and main factors influencing assessment of long-term pavement performance. Several key issues related to the quality control and quality assurance of the pavement roughness are discussed with reference to the verification techniques used by the MTO. Based on the literature review, future recommendations for possible improvements of the prediction models and techniques used for the evaluation of pavement performance are presented in order to obtain more consistent values.

“Off-the-Wall” Pavement Distress Variability Study

1998 ◽  
Vol 1643 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Jerome F. Daleiden ◽  
Amy L. Simpson
Keyword(s):  
Data Collection ◽  
Pavement Performance ◽  
Human Errors ◽  
Projection System ◽  
Pavement Distress ◽  
Sophisticated Equipment ◽  
Surface Distress ◽  
The Impact ◽  
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.

scholarly journals Flexible Pavement Performance Evaluation in Ontario: an Overview

2021 ◽  
Author(s):  
Muzaffar Hassan
Keyword(s):  
Management System ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Ride Quality ◽  
Physical Parameters ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Pavement Distress ◽  
Verification Techniques

Measuring pavement performance is a major component of the pavement management system. It assists in decision-making for finding the optimum strategies to provide, evaluate, and maintain serviceability in an acceptable condition cost effectively. The Ontario Ministry of Transportation (MTO) has been systematically rating pavement performance since the mid-1960s. Pavement condition survey involves measurement of two physical parameters: ride quality of pavement surfaces, and the extent and severity of pavement distress manifestations. The pavement ride quality can be measured with an acceptable level of consistency and repeatability through automation. However, achieving consistency in the evaluation of pavement distress manifestations is a challenging task because the automation that could accurately and consistently detect, quantify and record surface distresses is not fully developed is spite of rapid advances in video imagery and non-contact sensing devices. This report evaluates the progress made over the past three decades in the key areas of Distress Manifestation Index, Riding Comfort Rating, Pavement Condition Index and second generation Pavement Management System (PMS2). A review of the Ministryʼs network-level pavement performance database is presented, emphasizing pavement condition surveys, prediction models and main factors influencing assessment of long-term pavement performance. Several key issues related to the quality control and quality assurance of the pavement roughness are discussed with reference to the verification techniques used by the MTO. Based on the literature review, future recommendations for possible improvements of the prediction models and techniques used for the evaluation of pavement performance are presented in order to obtain more consistent values.

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

1997 ◽  
Vol 1592 (1) ◽  
pp. 151-168 ◽  
Author(s):  
Gonzalo R. Rada ◽  
Rajesh K. Bhandari ◽  
Gary E. Elkins ◽  
William Y. Bellinger
Keyword(s):  
Prediction Models ◽  
Survey Methods ◽  
Ground Truth ◽  
Condition Index ◽  
Pavement Performance ◽  
Severity Level ◽  
Data Variability ◽  
Rater Variability ◽  
The Individual

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.

scholarly journals Comparing of Data Collection for Network Level Pavement Management of Urban Roads and Highways

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Zhoucong Xu ◽  
Lei Yue
Keyword(s):  
Data Collection ◽  
Prediction Models ◽  
Condition Index ◽  
Current Data ◽  
Performance Model ◽  
Pavement Management ◽  
Pavement Performance ◽  
Pavement Condition ◽  
Monitoring Frequency ◽  
Maintenance Decisions

Pavement condition data are collected by agencies to support pavement management system (PMS) for decision-making purpose as well as to construct performance model. The cost of pavement data collection increases with the increase of survey frequencies. However, a lower monitoring frequency could lead to unreliable maintenance decisions. It is necessary to understand the influence of monitoring frequencies on maintenance decision by considering the reliability of performance prediction models. Because of different maintenance conditions of urban roads and highways, their performance show different trends. In this paper, the influence of pavement monitoring frequency on the pavement performance models was investigated. The results indicate that low collection frequencies may result in delay in maintenance action by overestimating pavement performance. The collection frequency for Pavement Condition Index (PCI) can be reduced without compromising the accuracy of performance model, more work should be done to ensure the PCI data quality, thus to guarantee the rationality of maintenance decisions. Effect of frequency reduction on pavement performance (IRI) models of urban roads seems greater than on pavement performance (IRI) models of highways, which may lead to heavier monitoring work for urban roads management. This paper provided an example which demonstrated how a comparative analysis can be performed to determine whether the current data collection plan can provide sufficient data for time series analysis.

Network-Level Pavement Performance Prediction Model Incorporating Censored Data

2003 ◽  
Vol 1853 (1) ◽  
pp. 72-79 ◽  
Author(s):  
Rodney R. DeLisle ◽  
Pasquale Sullo ◽  
Dimitri A. Grivas
Keyword(s):  
Censored Data ◽  
Performance Prediction ◽  
Prediction Models ◽  
New York State ◽  
Pavement Performance ◽  
Actual Performance ◽  
Pavement Condition ◽  
Condition Rating ◽  
Performance Curves ◽  
Pavement Performance Prediction

A methodology is presented for network-level pavement performance prediction that incorporates censored condition data. Censoring occurs when the duration at a specific condition level is not completely observed. This happens when pavement condition is improved and for the duration of the latest condition rating on file for each highway section. Pavement condition history files may contain significant quantities of censored data, yet such data typically are excluded when performance curves are developed. As a result, estimated condition durations and corresponding deterioration curves include deterioration rates that are greater, sometimes substantially greater, than those actually observed. The primary purpose for developing the presented methodology is to correct this shortcoming. Methodology development was facilitated with the use of a comprehensive information basis containing up to 20 years of historical pavement condition data for approximately 19,000 highway sections maintained by the New York State Department of Transportation. Durations at each condition rating were determined for each highway section over the 20-year period, with distinctions made between censored and uncensored observations. A modeling approach, with probability plotting and parameter estimation, was developed that resulted in performance curves. Differences in pavement performance based on geographic region were also investigated. From results obtained with the developed methodology, the main conclusion of this study is that accommodating censored data in pavement performance prediction models not only is feasible but better describes actual performance than if the data were simply excluded from the analysis.

Pavement Management Systems: Past, Present, and Future

2003 ◽  
Vol 1853 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Ram B. Kulkarni ◽  
Richard W. Miller
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Systems ◽  
Seamless Integration ◽  
Positioning Systems ◽  
Pavement Management Systems ◽  
Transportation Organization ◽  
Pavement Performance Prediction

The progress made over the past three decades in the key elements of pavement management systems was evaluated, and the significant improvements expected over the next 10 years were projected. Eight specific elements of a pavement management system were addressed: functions, data collection and management, pavement performance prediction, economic analysis, priority evaluation, optimization, institutional issues, and information technology. Among the significant improvements expected in pavement management systems in the next decade are improved linkage among, and better access to, databases; systematic updating of pavement performance prediction models by using data from ongoing pavement condition surveys; seamless integration of the multiple management systems of interest to a transportation organization; greater use of geographic information and Global Positioning Systems; increasing use of imaging and scanning and automatic interpretation technologies; and extensive use of formal optimization methods to make the best use of limited resources.

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