The performance of SBS (Styrene Butadiene Styrene) modified asphalt mixtures can be enhanced through the addition of fibers including basalt, ceramic, and glass. This study investigates whether a reduced SBS content of 3%, combined with 0.3% fiber reinforcement can match or exceed the performance of a traditional 7% SBS mixture. A comparative analysis was carried out by examining both performance efficiency and life cycle costs across ceramic, basalt, and glass fiber-reinforced mixtures. Maintenance requirements for each scenario were factored into the life cycle analysis. To assess structural integrity, 3D finite element simulations were conducted using the Burger’s logit model while focusing on fatigue and rutting damage. Findings indicate that basalt and ceramic fiber mixtures deliver better asphalt mixtures, thereby outperforming the 7% SBS mix by requiring fewer maintenance interventions. However, due to the higher cost of ceramic fiber mixtures at 831 Eur/m³, basalt fiber emerges as the more cost-effective option, achieving a performance efficiency gain of 20% with reduced costs at 532 Eur/m³. Among the fiber-reinforced variants, glass fiber showed the least improvement in performance, with a difference in 11% and 13% when compared to ceramic fiber and basal fiber, respectively.

The incorporation of basalt fiber into asphalt mixtures offers potential improvements in their viscoelastic properties. This study explores the addition of basalt fiber to Styrene Butadiene Styrene (SBS)-modified asphalt mixtures with varying SBS contents. Specifically, 0.3% basalt fiber was added to an asphalt mixture containing 3% SBS, and its performance, measured in terms of dynamic stability and flexural strength, was compared with a mixture with 7% SBS content. Additionally, finite element analysis using the Modified Burger’s Logit model was conducted to assess rutting and fatigue behavior. Given the high cost associated with increasing the SBS content, basalt fiber presents a cost-effective alternative without sacrificing performance. Laboratory tests, including the Marshall stability test, dynamic stability, flexural strength, and fatigue tests, were conducted to evaluate both mixtures. Results indicate that the mixture with 0.3% basalt fiber and 3% SBS outperforms the 7% SBS mixture, showing a 47% improvement in dynamic stability and rutting resistance and a 16% increase in flexural strength.

Featured Applications: Pavement texture evaluation using a traditional sand patch method, 3D laser scanning, and machine learning algorithms. Pavement macrotexture, quantified by mean texture depth (MTD) and mean profile depth (MPD), is a critical parameter for road safety and performance. The traditional sand patch test is labor-intensive and slow, creating a bottleneck for modern pavement management systems. Accurately translating the rich point cloud data into reliable MTD values using the 3D scanning method remains a challenge, with current methods often relying on oversimplified correlations. This research addresses this gap by developing and validating a novel machine learning framework to predict MTD and MPD directly from high-resolution 3D laser scans. A comprehensive dataset of 127 pavement samples was created, combining traditional sand patch measurements with detailed 3D point clouds. From these point clouds, 27 distinct surface features spanning statistical, spatial, spectral, and geometric domains were developed. Six machine learning algorithms, consisting of Random Forest, Gradient Boosting, Support Vector Regression, k-Nearest Neighbor, Artificial Neural Networks, and Linear Regression, were implemented. The results demonstrate that the ensemble-based Random Forest model achieved superior performance, predicting MTD with an R² of 0.941 and a mean absolute error (MAE) of 0.067 mm, representing a 56% improvement in accuracy over traditional digital correlation methods. Model interpretation via SHAP analysis identified root mean square height (Sq) and surface skewness (Ssk) as the most influential features.