Roughness Progression Model for Asphalt Pavements Using Long-Term Pavement Performance Data

Meegoda, Jay N.; Gao, Shengyan

doi:10.1061/(asce)te.1943-5436.0000682

Cited by 50 publications

(20 citation statements)

References 3 publications

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“…The observed R values are 0.724 and 0.456 for Equations 1 and 3, respectively. However, the enhanced dummy regression equation developed in this study showed a better prediction with only 17.6% difference between the measured and predicted mean IRI values compared to the model developed by Meegoda and Gao (2014). Further analysis on the data sets from the test sections in Wyoming showed that the R values between 0.2 to 0.5.…”

Section: Verification Of the Enhanced Dummy Regression Equation For Tmentioning

confidence: 64%

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Pavement Roughness Modeling Using Regression And Ann Methods For LTPP Western Region

Najjar¹,

Uddin²,

Jaafar³

2019

The European Proceedings of Multidisciplinary Sciences

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This study aims to develop pavement roughness models using multiple linear regression equation and artificial neural network (ANN) modeling approaches. The model database uses International Roughness Index (IRI) data included in a national database for the Western region. Datasets for asphalt pavement with bound base at 32 different locations are considered in the analysis. The variables included are IRI, pavement age, design structural number, equivalent single axle load (ESAL), and also a dummy variable for construction number. The LTPP data was used to compare predicted IRI values using the improved linear regression equation with R of 0.573, with different types of ANN models. The ANN models considered are static ANN, feedback ANN, and dynamic ANN. The verifications of the improved dummy regression equation predictions for 39 data points showed only-2.9% difference compared to the mean IRI. Comparisons between the mean predicted IRI values showed that the enhanced dummy regression equation gave a better prediction compared to the ANN model with a minimum error of 37.9%. Out of three ANN models, the feedback ANN provided a better prediction compared to the static and dynamic ANNs. Further analysis showed that the predictions using training all data sets are more accurate with R ranged from 0.740 to 0.827. It is important to consider ESAL and construction number for an accurate and reliable future IRI prediction.

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Section: Verification Of the Enhanced Dummy Regression Equation For Tmentioning

confidence: 64%

“…Comparison with Meegoda and Gao (2014) 548 regression equation showed a better prediction using the data sets for test sections in California and Wyoming.…”

Section: Resultsmentioning

confidence: 99%

Pavement Roughness Modeling Using Regression And Ann Methods For LTPP Western Region

Najjar¹,

Uddin²,

Jaafar³

2019

The European Proceedings of Multidisciplinary Sciences

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“…Hazard-based duration modeling is a practical technique for survival analysis in transportation. It has been used to model cracking (23)(24)(25), roughness (26), general pavement performance (27), and the effectiveness of pavement overlays (28). Because friction is similar to those pavement performance measures, survival analysis is a logical choice for modeling the degradation process.…”

Section: Methodsmentioning

confidence: 99%

Pavement Friction Degradation Based on Pennsylvania Field Test Data

Guler

Donnell

2017

Transportation Research Record

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Pavement surface–tire friction is a critical safety element associated with roadway design, construction, and maintenance practices. The skid resistance of pavements generally declines over time and increases the risk of skidding-related crashes. On horizontal curves, lateral friction may be associated with lane-departure incidents, particularly as the pavement ages and drivers demand more lateral friction than the pavement surface–tire interaction can supply. On tangent roadway sections, longitudinal friction affects braking distances. As the skid-resistance properties of a pavement surface decline over time, braking distances increase, and may increase risks to driver safety. A comprehensive understanding of the process of pavement friction degradation could help highway agencies identify roadway segments that need maintenance to reduce the probability of skid-related incidents. This paper presents a survival analysis of friction degradation for asphalt pavement surfaces. Duration models were estimated with data collected annually along an Interstate highway in Pennsylvania to investigate the degradation of friction over time. These models consider traffic volume and roadway features to determine the probability that friction levels will remain above various friction thresholds. The resulting statistical models can help transportation agencies make better decisions about pavement maintenance to reduce safety risk.

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“…IRI (international roughness index) and rut depth data are not recommended to be collected annually on county roads for lower tra c volumes [2]. IRI is widely accepted and used for evaluating pavement condition, establishing performance models and performing cost-e ectiveness analysis of maintenance strategies [3][4][5][6]. However, it is proved that IRI cannot be a unique predictor of pavement condition ratings with 41% of the variation in PCI (pavement condition index) remains unaccounted by IRI [7].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Yue

2020

Journal of Advanced Transportation

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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.

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Roughness Progression Model for Asphalt Pavements Using Long-Term Pavement Performance Data

Cited by 50 publications

References 3 publications

Pavement Roughness Modeling Using Regression And Ann Methods For LTPP Western Region

Pavement Roughness Modeling Using Regression And Ann Methods For LTPP Western Region

Pavement Friction Degradation Based on Pennsylvania Field Test Data

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

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