Data Analysis Procedures for Long-Term Pavement Performance Prediction

1996 ◽  
Vol 1524 (1) ◽  
pp. 152-159 ◽  
Author(s):  
H. R. Kerali ◽  
A. J. Lawrance ◽  
K. R. Awad
Keyword(s):  
Data Analysis ◽  
Layer Thickness ◽  
Material Properties ◽  
Pavement Performance ◽  
Model Parameters ◽  
Model Form ◽  
Cubic Model ◽  
Pavement Structures ◽  
Pavement Performance Prediction

The results of 3 years of research aimed at investigating data analysis methods used in the development of pavement performance relationships are reported. The research was part of the U.K. collaborative program linked to the U.S. Strategic Highway Research Program (SHRP), in particular the Long-Term Pavement Performance (LTPP) experiment. The development of pavement performance models usually concludes with the application of regression techniques to determine coefficients for model parameters. It is important to identify the model forms and the engineering or mechanistic principles to be used in the data analyses in the initial stages and then to censor any obvious anomalies in the data. This was applied to data on pavement rutting measured by the Transport Research Laboratory over a 20-year period in the United Kingdom. Engineering knowledge of rutting progression suggests a cubic model form, with the quadratic component representing typical performance in early pavement life. An attempt was made to derive a rutting model that took into account material properties, layer thickness, and aggregate types. The pavement structural number concept was applied as a proxy for pavement strength for the different pavement structures used in the test sites. The results of the analyses confirmed that material properties, layer thickness, and their combined effects influence rutting, but in ways that vary greatly. No simple model form was found to adequately predict rutting for a variety of pavement types, even with general categorical model forms.

Use of Long-Term Pavement Performance Data to Develop Traffic Defaults in Support of Mechanistic-Empirical Pavement Design Procedures

2003 ◽  
Vol 1855 (1) ◽  
pp. 176-182 ◽  
Author(s):  
Weng On Tam ◽  
Harold Von Quintus
Keyword(s):  
Pavement Design ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Traffic Data ◽  
Design Procedures ◽  
Load Spectra ◽  
State Highway ◽  
Pavement Structures ◽  
Empirical Design

Traffic data are a key element for the design and analysis of pavement structures. Automatic vehicle-classification and weigh-in-motion (WIM) data are collected by most state highway agencies for various purposes that include pavement design. Equivalent single-axle loads have had widespread use for pavement design. However, procedures being developed under NCHRP require the use of axle-load spectra. The Long-Term Pavement Performance database contains a wealth of traffic data and was selected to develop traffic defaults in support of NCHRP 1-37A as well as other mechanistic-empirical design procedures. Automated vehicle-classification data were used to develop defaults that account for the distribution of truck volumes by class. Analyses also were conducted to determine direction and lane-distribution factors. WIM data were used to develop defaults to account for the axle-weight distributions and number of axles per vehicle for each truck type. The results of these analyses led to the establishment of traffic defaults for use in mechanistic-empirical design procedures.

Seed Modulus Generation Algorithm for Backcalculation of Flexible Pavement Moduli

2005 ◽  
Vol 1905 (1) ◽  
pp. 117-127 ◽  
Author(s):  
T. F. Fwa ◽  
Thakur Swapna Rani
Keyword(s):  
Closed Form ◽  
Flexible Pavements ◽  
Flexible Pavement ◽  
Pavement Performance ◽  
Program Performance ◽  
Generation Algorithm ◽  
User Input ◽  
Pavement Structures ◽  
Performance Project

The seed moduli chosen for backcalculation analysis of multilayer flexible pavements can have significant impacts on the performance of backcalculation software and, sometimes, the final solutions of the backcalculated moduli. Practically all backcalculation programs provide internally generated seed moduli for backcalculation analysis. However, as the internally generated seed moduli do not always produce satisfactory results, the use of user-input seed moduli is generally encouraged. With the aim of providing useful guidance in the choice of seed moduli, a seed modulus generation algorithm, 2L-BACK, for multilayer flexible pavements based on a closed-form modulus backcalculation solution for two-layer flexible pavement structures was developed. The proposed algorithm does not require any subjective judgment by the user. An evaluation analysis of the effectiveness of the proposed procedure is presented by the use of two types of backcalculation software, MICHBACK and EVERCALC, and is based on measured and computed data for flexible pavement segments from the Long-Term Pavement Performance project. A comparison was made of the backcalculation program performance and the computed moduli of solutions obtained from internally generated seed moduli and those obtained from seed moduli generated by the proposed algorithm. It was found that the proposed seed modulus generation algorithm led to enhanced program performance of MICHBACK with respect to convergence characteristics and the accuracies of the backcalculated solutions. In comparison, the corresponding improvements for the case of EVERCALC were less. The proposed seed modulus generation algorithm does not suffer from the location and pavement type transferability constraints of most regression-based seed modulus generation methods. The results of the study suggest that the algorithm can be effectively incorporated into backcalculation software for multilayer flexible pavements.

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.

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.

scholarly journals Effect of variations in layer thickness and resilience modulus on flexible pavement performance

2021 ◽  
Vol 10 (8) ◽  
pp. e42610817466
Author(s):  
Thaís Ferrari Réus ◽  
Heliana Barbosa Fontenele
Keyword(s):  
Layer Thickness ◽  
Design Method ◽  
Flexible Pavement ◽  
Empirical Method ◽  
Performance Criteria ◽  
Pavement Performance ◽  
Layered System ◽  
State Highway ◽  
Pavement Performance Prediction ◽  
Resilience Modulus

A pavement mechanistic-empirical analysis is based on a pre-designed structure checked for required performance criteria. In case the latter are not met, this structure is modified and reprocessed. In this context, analyzing the effect of variations in project parameters on pavement performance prediction subsidizes a better understanding of results provided by computer programs. The objective of this study is to assess the effect of layer thickness and resilience modulus variations on flexible pavement performance. To do so, performance was estimated for the 20th project year through Elastic Layered System Model 5 (ELSYM5) software and American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical method (ME). Using multiple regression models for result adjustment and through statistical assessments on regression coefficients calculated, it can be concluded that pavement lifespan consumption, predicted by simulations on ELSYM5, is sensitive to variations in coating and subbase thickness and in subgrade resilience modulus. For AASHTO ME method, predicted values for distresses were significantly sensitive to variations in coating, base and subbase thickness, and in base and subgrade resilience modulus. Comparing both approaches, it is concluded that ELSYM5 can be a viable alternative to the application of a ME pavement design method.

scholarly journals Panel data analysis of surface skid resistance for various pavement preventive maintenance treatments using long term pavement performance (LTPP) data

2017 ◽  
Vol 44 (5) ◽  
pp. 358-366 ◽  
Author(s):  
Qiang Joshua Li ◽  
You Zhan ◽  
Guangwei Yang ◽  
Kelvin C.P. Wang ◽  
Chaohui Wang
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Panel Data ◽  
Preventive Maintenance ◽  
Panel Data Analysis ◽  
Pavement Performance ◽  
Skid Resistance ◽  
Cross Sectional ◽  
Slurry Seal

Various preventive maintenance (PM) treatments have been employed to restore pavement skid resistance for enhanced safety. This paper investigates the effectiveness of PM treatments using panel data analysis (PDA). Panel data analysis investigates the differences of cross-sectional information among treatments, but also the time-series changes within each treatment over time. Panel data with multiple years of friction data for four treatments (thin overlay, slurry seal, crack seal, and chip seal) at various climate, traffic, and pavement conditions are obtained from 255 long term pavement performance (LTPP) testing sections. Both fixed- and random-effects models are developed to evaluate pavement skid resistance performance and to identify the most influencing factors. Results from the PDA models are compared to those from traditional ordinary regression models. Slurry seal is demonstrated to be the most effective treatment. Five factors (precipitation, freezing index, humidity, traffic, and pavement age) are identified to be significant for pavement friction. Fixed-effects panel model is selected for the development of friction prediction models. This study not only demonstrates the capability of PDA for analyzing friction data with cross-sectional and time-series characteristics, but also can assist engineers in selecting the most effective PM treatments for the desired level of skid resistance to reduce traffic crashes.

Coming Together Through Falling Apart

2019 ◽  
Vol 40 (2) ◽  
pp. 151-168
Author(s):  
Heather Churchill ◽  
Jeremy M. Ridenour
Keyword(s):  
Data Analysis ◽  
Qualitative Data ◽  
Structural Changes ◽  
Systematic Approach ◽  
Qualitative Approach ◽  
Psychological Assessments ◽  
Transference And Countertransference ◽  
Testing Data ◽  
Psychoanalytically Oriented Psychotherapy

Abstract. Assessing change during long-term psychotherapy can be a challenging and uncertain task. Psychological assessments can be a valuable tool and can offer a perspective from outside the therapy dyad, independent of the powerful and distorting influences of transference and countertransference. Subtle structural changes that may not yet have manifested behaviorally can also be assessed. However, it can be difficult to find a balance between a rigorous, systematic approach to data, while also allowing for the richness of the patient’s internal world to emerge. In this article, the authors discuss a primarily qualitative approach to the data and demonstrate the ways in which this kind of approach can deepen the understanding of the more subtle or complex changes a particular patient is undergoing while in treatment, as well as provide more detail about the nature of an individual’s internal world. The authors also outline several developmental frameworks that focus on the ways a patient constructs their reality and can guide the interpretation of qualitative data. The authors then analyze testing data from a patient in long-term psychoanalytically oriented psychotherapy in order to demonstrate an approach to data analysis and to show an example of how change can unfold over long-term treatments.

Becoming Rhizomatic: Researching Flowing in/between Striated and Smooth Space

2019 ◽  
Vol 13 (3) ◽  
pp. 355-376
Author(s):  
Ester A. Betrián Villas ◽  
Gloria Jové Monclus ◽  
Charly Ryan
Keyword(s):  
Data Analysis ◽  
Educational Change ◽  
Research Methodology ◽  
Deleuze And Guattari ◽  
Smooth Space

Exploring long-term educational change, we investigate our re/construction of research methodology as we moved from a positivist framework to working with ideas drawn from Deleuze and Guattari. We reveal our becoming rhizomatic in data analysis in the metamodelling of the richness flowing horizontally through our practices. We tell of our struggles to escape hierarchical thinking and relations researching between the smooth and striated. A space of interactions, conversations and writings created relations between polyphonic voices, leading us to an emergent methodology. Our struggle against hierarchies in data analysis yielded rich educational possibilities for becoming that Deleuzo-Guattarian thinking offers us.

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