Development of Prediction Models for Joint Faulting Using Long-Term Pavement Performance Database

[[abstract]]The main objective of this study is to develop improved faulting prediction models for jointed concrete pavements using the Long-Term Pavement Performance (LTPP) database. The retrieval, preparation, and cleaning of the database were carefully handled in a systematic and automatic approach. The prediction accuracy of the existing prediction models implemented in the recommended Mechanistic-Empirical Pavement Design Guide (NCHRP Project 1-37A) was found to be inadequate. Exploratory data analysis of the response variables indicated that the normality assumption with random errors and constant variance using conventional regression techniques might not be appropriate for prediction modeling. Therefore, without assuming the error distribution of the response variable, several modern regression techniques including generalized linear model (GLM) and generalized additive model (GAM) along with quasi-likelihood estimation method and Poisson distribution were adopted in the subsequent analysis. Box-Cox power transformation and visual graphical techniques were frequently adopted during the prediction modeling process. By keeping only those parameters with significant effects and reasonable physical interpretations in the model, various tentative performance prediction models were developed. The resulting mechanistic-empirical model included several variables such as pavement age, yearly ESALs, bearing stress, annual precipitation, base type, subgrade type, annual temperature range, joint spacing, modulus of subgrade reaction, and freeze-thaw cycle for the prediction of joint faulting. The goodness of fit was further examined through the significant testing and various sensitivity analyses of pertinent explanatory parameters. The tentatively proposed predictive models appeared to reasonably agree with the pavement performance data although their further enhancements are possible and recommended.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]TW

Preliminary Analysis of Flexible Pavement Performance Data Using Linear Mixed Effects Models

[[abstract]]Multilevel data are very common in many fields. Because of its hierarchical data structure, multilevel data are often analyzed using Linear Mixed-Effects (LME) models. The exploratory analysis, statistical modeling, and the examination of model-fit of LME models are more complicated than those of standard multiple regressions. A systematic modeling approach using visual-graphical techniques and LME models was proposed and demonstrated using the original AASHO road test flexible pavement data. The proposed approach including exploring the growth patterns at both group and individual levels, identifying the important predictors and unusual subjects, choosing suitable statistical models, selecting a preliminary mean structure, selecting a random structure, selecting a residual covariance structure, model reduction, and the examination of the model fit was further discussed.[[booktype]]紙

Development of Fatigue Cracking Prediction Models Using Long-Term Pavement Performance Database

[[abstract]]This study strives to develop improved fatigue cracking models using the long-term pavement performance database. The prediction accuracy of the existing models was found to be inadequate. Several modern regression techniques including generalized linear model and generalized additive model along with the assumption of Poisson distribution and quasi-likelihood estimation method were adopted for the modeling process. After many trials in eliminating insignificant and inappropriate parameters, the resulting model included several variables such as yearly KESALs), pavement age, annual precipitation, annual temperature, critical tensile strain under the asphalt-concerete surface layer, and freeze-thaw cycle for the prediction of fatigue cracking. The proposed model appeared to have substantial improvements over the existing models although their further enhancements are possible and recommended

Prediction Models for Transverse Cracking of Jointed Concrete Pavements: Development with Long-Term Pavement Performance Database

[[abstract]]The main objective of this study was to develop improved prediction models for transverse cracking of jointed concrete pavements with the Long-Term Pavement Performance database. The retrieval, preparation, and cleaning of the database were carefully handled with a systematic and automatic approach. The prediction accuracy of the existing prediction models implemented in the recommended MechanisticâEmpirical Pavement Design Guide (NCHRP Project 1-37A) was found to be inadequate. Exploratory data analysis indicated that the normality assumption with random errors and constant variance by using conventional regression techniques might not be appropriate for this study. Therefore, several modern regression techniques, including the generalized linear model and the generalized additive model, along with the assumption of Poisson distribution, were adopted for the modeling process. The resulting mechanisticâempirical model included several variablesâsuch as pavement age, yearly equivalent single-axle loads (ESALs), accumulated ESALs, annual precipitation, freezeâthaw cycle, annual temperature range, stress ratio, and percent steelâfor the prediction of transverse cracking. The goodness of fit was further examined through significant testing and various sensitivity analyses of pertinent explanatory parameters. The tentatively proposed predictive models appeared to agree reasonably with the pavement performance data, although their further enhancements are possible and recommended.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙本[[booktype]]電子

Application of Regression Spline in Multilevel Longitudinal Modeling

175 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002.A general description of linear mixed-effects models, linear mixed-effects models for longitudinal data, and regression splines are first reviewed. Subsequently, a methodology that applies regression spline in multilevel longitudinal modeling is proposed to deal with the longitudinal data with large numbers of time points. The visual-search data are used to demonstrate the proposed methodology. Several recommendations on the application of regression spline in multilevel longitudinal modeling are discussed.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Preliminary Analysis of AASHO Road Test Rigid Pavement Data Using Modern Regression Techniques

NSC 101-2221-E-032-033[[abstract]]The normality assumptions with random errors and constant variance were often violated while analyzing multilevel pavement performance data using conventional regression techniques. Because of its hierarchical data structure, multilevel data are often analyzed using Linear Mixed-Effects (LME) models. The exploratory analysis, statistical modeling, and the examination of model-fit of LME models are more complicated than those of standard multiple regressions. A systematic modeling approach using visual-graphical techniques and LME models was proposed and demonstrated using the original AASHO road test rigid pavement data. The basic modeling approach includes: selecting a preliminary mean structure, selecting a random structure, selecting a residual covariance structure, model reduction, and examining the model fit. A goodness of fit plot indicates that the preliminary LME model provides better explanation to the data.[[notice]]補正完畢[[incitationindex]]EI[[conferencetype]]國際[[booktype]]紙本[[iscallforpapers]]

Closure to “Development of Fatigue Cracking Prediction Models Using Long-Term Pavement Performance Database” by Hsiang-Wei Ker, Ying-Haur Lee, and Pei-Hwa Wu

[[abstract]]The article presents clarifications of the article "Development of Fatigue Cracking Prediction Models Using Long-Term Pavement Performance Database," by Hsiang-Wei Ker, Ying-Haur Lee and Pei-Hwa Wu, published in the 2008 issue. It explains several key points in the article, including the phenomenon of fatigue mechanism of asphalt pavement and the distribution of the actual fatigue cracking. The article also discusses the equations used in ascertaining the load repetition and pavement age.[[incitationindex]]SCI[[booktype]]紙