prikaz prve stranice dokumenta Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters
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Scientific paper - Original scientific paper
Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters
Lubricants, 12 (2024), 1; 23. https://doi.org/10.3390/lubricants12010023
Ban, Ivana; Deluka-Tibljaš, Aleksandra; Ružić, Igor

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Ban, I., Deluka Tibljaš, A. & Ružić, I. (2024). Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters. Lubricants, 12. (1). doi: 10.3390/lubricants12010023

Ban, Ivana, et al. "Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters." Lubricants, vol. 12, no. 1, 2024. https://doi.org/10.3390/lubricants12010023

Ban, Ivana, Aleksandra Deluka Tibljaš and Igor Ružić. "Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters." Lubricants 12, no. 1 (2024). https://doi.org/10.3390/lubricants12010023

Ban, I., Deluka Tibljaš, A. and Ružić, I. (2024) 'Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters', Lubricants, 12(1). doi: 10.3390/lubricants12010023

Ban I, Deluka Tibljaš A, Ružić I. Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters. Lubricants [Internet]. 2024 January 12 [cited 2025 March 25];12(1). doi: 10.3390/lubricants12010023

I. Ban, A. Deluka Tibljaš and I. Ružić, "Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters", Lubricants, vol. 12, no. 1, January 2024. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:157:077459. [Accessed: 25 March 2025]

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Metadata
Title (english)Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters
AuthorIvana Ban
AuthorAleksandra Deluka-Tibljaš
AuthorIgor Ružić
Author's institutionUniversity of Rijeka
Faculty of Civil Engineering
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Transportation
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleLubricants
Numberingvol. 12, no. 1, 23
e-ISSN2075-4442
DOIhttps://doi.org/10.3390/lubricants12010023
URN:NBNurn:nbn:hr:157:077459
Publication2024-01-12
ProjectNumber: uniri-tehnic-18-143 – 1289
Title (croatian): Prometna infrastruktura u funkciji održive urbane mobilnosti
Title (english): Transport infrastructure in the function of sustainable mobility
Leader: Aleksandra Deluka-Tibljaš
Jurisdiction: Croatia
Document URLhttps://www.mdpi.com/2075-4442/12/1/23
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2025-03-25 08:49:32