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Machine‑learning prediction of skid resistance by British Pendulum Tester from 3D pavement surface texture: assessing the relative contributions of micro‑ and macrotexture

Year: 2026
Type: article
Abstract: Skid resistance is strongly influenced by pavement surface texture, yet the texture–friction relationship is nonlinear and scale dependent. This study evaluates prediction of Pendulum Test Value (PTV) from 3D pavement-surface texture descriptors and quantifies the relative roles of microtexture and macrotexture. A dataset of 872 co-located texture–friction samples from 45 asphalt pavement test sections was used to train multivariable regression models on microtexture-only, macrotexture-only, and combined predictor sets. Across all inputs, nonlinear methods outperformed linear regression. On the combined set, support vector regression achieved the best accuracy (R2 = 0.82 ± 0.03; RMSE = 4.8 ± 0.3 PTV), followed closely by random forest (R2 = 0.78 ± 0.03; RMSE = 5.2 ± 0.4), compared with the linear baseline (R2 = 0.60 ± 0.04; RMSE = 7.0 ± 0.4). Under strict section-wise validation (leave-one-section-out and 5-fold group cross-validation), out-of-section performance decreases (for the RF reference model on the COMBINED set, R2 ≈ 0.46–0.50 and RMSE ≈ 8 PTV), highlighting limited transfer to unseen sections and the need for broader training data. Combining microtexture and macrotexture systematically improved performance over single-scale models, indicating complementary predictive information. Permutation-based interpretation with random forests identified Micro-Smrk2 as the most influential descriptor, with additional contributions from both texture scales. Group-level perturbation showed that microtexture is more critical for predictive accuracy on held-out data, while macrotexture provides non-negligible complementary signal.
DOI: 10.1080/10298436.2026.2728068
Language: English
Open Access status: hybrid
OpenAlex ID: W7207515356
FWCI:
Cites: 37
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