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

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.