230
Views
5
CrossRef citations to date
0
Altmetric
Articles

Novel approach to pavement friction analysis with advanced statistical methods using structural equation modelling

&
Pages 236-245 | Received 11 Nov 2017, Accepted 13 Mar 2018, Published online: 20 Apr 2018
 

ABSTRACT

Pavement skid resistance has a significant role in traffic accidents, especially in wet conditions. Pavement surface characteristics are affected by both materials and mixture properties. This study explored a ‘novel’ approach to pavement friction analysis in modelling and relating pavement friction to materials and mixture properties. Structural equation modelling (SEM) takes advantage of the correlation/collinearity among one or more predictor variables in generating predictive models for a response variable. While SEM has been used in a variety of fields, in pavement friction the use of such statistic approach has not been explored, and thus it is a ‘novel approach’ to pavement friction modelling in relation to the past modelling efforts. Thus, in this study the selection of SEM modelling is advantageous so as to, (i) capture the interdependency of mixture and material variables in hot mix asphalts; and (ii) address the high number of predictor variables in relation to the number of observations (small sample size of observations). While data from Maryland were used in this analysis the methodology can be used elsewhere reflecting similar materials and pavement conditions.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 225.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.