Year: 2026
Type: article
Abstract: Porous asphalt pavements play an important role in stormwater management while also providing structural integrity. Reliable permeability prediction is dynamic, allowing for better design optimization, long-term functioning evaluation, and effective maintenance planning. This work creates and evaluates artificial intelligence (AI) models for predicting the hydraulic permeability of porous asphalt pavements using a multi-regional dataset comprising 1,250 laboratory and field observations. The information included mix design parameters (air voids, D10/D50 ratio, binder concentration), compaction, construction, climate, and maintenance history. Machine-learning techniques such as artificial neural networks (ANN), support vector machines (SVM), random forest, and gradient boosting were trained and tested using 10-fold cross-validation and temporal validation (2010–2017 training; 2018–2021). ANN provided the greatest performance (R² = 0.91, RMSE ≈ 0.11 cm/s) with a
DOI: 10.63841/iue3si176
Institution: Akre University for Applied Sciences
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7213435353