Deterioration Characteristics of Waterborne Pavement Markings Subjected to Different Operating Conditions

Mohamed, Maged; Skinner, Andrew; Abdel-Rahim, Ahmed; Kassem, Emad; Chang, Kevin

doi:10.1061/jpeodx.0000101

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…The modulus back-calculation method used is based on a flexible pavement which, theoretically, is not applicable to rigid PCC pavement [25]. Secondly, the mechanical degradation approach, which is based on a fatigue criterion for concrete materials, does not consider the combination of structure and thickness [26,27]. Lastly, the statistical approach is the most direct and most closely related to the field situation, but has considerable drawbacks, including that the time taken for statistical analysis is extensive, and the failure time is difficult to accurately assess.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A Fast and Non-Destructive Prediction Model for Remaining Life of Rigid Pavement with or without Asphalt Overlay

et al. 2022

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Remaining life is an important indicator of pavement residual effective service time and is directly related to maintenance decision-making with limited funds. This paper proposes a fast and non-destructive model to predict the remaining life of rigid PCC (Portland cement concrete) pavement, with or without asphalt overlay. Firstly, a model was constructed according to the current Chinese design specifications for concrete pavement integrating an inverse design concept. Secondly, the prediction model was applied to three typical pavement sections with 1430, 1250 and 1000 slabs, respectively. Ground penetrating radar (GPR) was utilized to determine the geometric parameters in the predictive model and the physical state of the pavement. A falling weight detector (FWD) was utilized for determination of the mechanical parameters. A more reasonable equivalent elastic modulus of foundation was back-calculated instead of using the limited model in the design specification. Thirdly, the remaining life was predicted based on the current mechanical and geometric parameters. The distributions of the remaining life of the three pavement sections was statistically analyzed. Finally, a decision-making system to inform maintenance strategy was proposed based on the remaining life and the technical condition of each slab. The results showed that the relationship between the remaining life and the mechanical parameters, geometric parameters and the physical state of the pavement was highly consistent with engineering experience. The success rate of the prediction model was as high as 96%. The proposed fast and non-destructive prediction model showed good engineering applicability and feasibility. The decision-making system was shown to be feasible in terms of economic benefits.

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Section: Introduction 1backgroundmentioning

confidence: 99%

A Fast and Non-Destructive Prediction Model for Remaining Life of Rigid Pavement with or without Asphalt Overlay

et al. 2022

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“…The explanatory variables for each model depend on experimental planning and data collection. As can be seen, there is no agreement on which explanatory variables best describe the retroreflectivity decrease, indicating that the degradation depends on several aspects of the experimental planning as well as the road characteristics ( 6 – 20 ).…”

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confidence: 99%

“…Table 1 summarizes relevant studies found in the literature ( 6 – 20 ) and presents the explanatory variables included in the model, the duration and location of each experiment, and the traffic volume. The explanatory variables for each model depend on experimental planning and data collection.…”

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confidence: 99%

“…Among the models found in the literature, the most common variable is the pavement marking age ( 6–8, 10–20 ). In addition, many authors have included the traffic volume as an explanatory variable for the pavement marking service life model and have correlated the traffic as a significant variable for retroreflectivity degradation ( 8, 10, 12–16, 19, 20 ).…”

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Probabilistic Service Life Model of Pavement Marking by Degradation Data

Mazzoni

Vasconcelos

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Pavement markings are important features for improving road safety. As these markings must possess adequate retroreflectivity, road agencies are concerned about retroreflectivity maintenance. Consequently there is an increased focus on modeling the durability of pavement markings. Such models use time-to-failure data from life test experiments. The current study proposes a new method that consists of three steps: (i) designing an accelerated experiment for data collection; (ii) fitting a pavement marking retroreflectivity model using a generalized linear mixed model and the degradation data from the planned experiment, confirming the goodness of fit of the fitted model through the residual analysis; and (iii) conducting Monte Carlo simulation (with at least 10,000 runs of simulated vectors of the fitted model’s coefficients) to obtain the pavement marking service life distribution for a failure retroreflectivity threshold level. The simulation of service life of pavement marking provides relevant information, such as service life expectation and empirical quantities of interest (as median). The proposed method is applied to data on retroreflectivity degradation obtained from an experimental test site, a Brazilian highway with a high traffic volume, with two fixed factors (waterborne paints and glass bead application rates, both with three levels). The model indicates that retroreflectivity decreases at an average rate of 2% per day, and how materials differ from each other. The proposed model is easily implementable and can help management teams to adequately plan the maintenance time for pavement markings.

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