Confirmatory factor analysis (CFA) was utilized to analyze the contributions of condition measurements on the latent comprehensive pavement condition and the relative importance of the condition measurements. Both single-factor and multiple-factor model were built. An exploratory factor analysis (EFA) was conducted to determine the latent factors of the multiple CFA model. The results indicated that the multiple-factor model provided better model fit goodness than the single-factor model. All the observed variables including IRI and other nine distress variables were statistically significant for the comprehensive pavement condition. The strong indicators such as IRI, longitudinal cracking in wheel path, non-wheel path longitudinal cracking, fatigue cracking had greater weightings on the pavement condition indices than the weak ones’. Three latent factors were suggested by the multiple-factor CFA model, representing ride quality, early age cracking and severe pavement deterioration, respectively. Comparison between state routes and Interstates indicated that the more severe distresses such as fatigue and transverse cracking have higher contributions to the latent pavement condition of state routes, while less severe distresses such as raveling and patch have higher contributions to Interstates. This is because Interstates are maintained at the higher condition level and the less severe or early age distresses have higher impacts on the overall condition.
Contributions of condition measurements on the latent pavement condition by confirmatory factor analysis
Transportmetrica A: Transport Science ; 15 , 1 ; 2-17
07.02.2019
16 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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