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- Title
Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters.
- Authors
Ban, Ivana; Deluka-Tibljaš, Aleksandra; Ružić, Igor
- Abstract
The importance of skid resistance performance assessment in pavement engineering and management is crucial due to its direct influence on road safety features. This paper provides a new approach to skid resistance predictive model definition based on experimentally obtained texture roughness parameters. The originally developed methodology is based on a photogrammetry technique for pavement surface data acquisition and analysis, named the Close-Range Orthogonal Photogrammetry (CROP) method. Texture roughness features were analyzed on pavement surface profiles extracted from surface 3D models, obtained by the CROP method. Selected non-standard roughness parameters were used as predictors in the skid resistance model. The predictive model was developed by the partial least squares (PLS) method as a feature engineering procedure in the regression analysis framework. The proposed model was compared to the simple linear regression model with a traditional texture parameter Mean Profile Depth as the predictor, showing better predictive strength when multiple non-standard texture parameters were used.
- Subjects
SKID resistance; PAVEMENT management; PREDICTION models; ENGINEERING management; INDUSTRIAL engineering; FEATURE selection; PARTIAL least squares regression
- Publication
Lubricants (2075-4442), 2024, Vol 12, Issue 1, p23
- ISSN
2075-4442
- Publication type
Article
- DOI
10.3390/lubricants12010023