Adnan Adhab K. Al-Saeedi

Crumb rubber (CR) modification offers a sustainable approach to improving asphalt performance while promoting the beneficial reuse of end-of-life tyres. This study evaluates the structural performance of CR-modified hot mix asphalt (HMA) under service conditions through an integrated mechanistic and machine-learning framework. Laboratory-measured resilient moduli for conventional and 15% CR-modified HMA at 20, 40, and 60 °C were incorporated into a three-layer Odemark–Shell analysis considering 11 asphalt-layer thicknesses and three representative seasonal temperature periods established from an 11-year (2015–2025) climatic record for Karbala, Iraq. Pavement performance was assessed for a 20-year design period under 6 × 10⁶ equivalent single axle loads (ESALs), with fatigue and rutting damage accumulated using Miner’s rule. Eight machine-learning algorithms were additionally evaluated as fast surrogate models for predicting the critical pavement responses. Incorporating CR increased the resilient modulus by 8.5–9.2%, resulting in reductions of 9.7–10.4% in horizontal tensile strain and 1.2–2.8% in vertical compressive strain over the investigated temperature range. The continuous interpolation of the fatigue-damage criterion indicated required asphalt thicknesses of approximately 27.3 cm for conventional HMA and 25.8 cm for CR-modified HMA, corresponding to a reduction of approximately 1.5 cm (5–6%). At the common 27.5 cm design thickness, CR modification reduced the governing fatigue damage index from 0.954 to 0.723, representing a 24.2% reduction in accumulated fatigue damage and increasing the estimated service life from approximately 21.0 to 27.7 years. Gradient Boosting achieved surrogate-model fidelity, with R² values of 0.993 and 0.9998 for the critical strain responses, while asphalt-layer thickness emerged as the dominant performance variable, consistent with the known analytical structure of the governing equations. The use of CR could additionally divert approximately 10.9 tonnes of scrap tyre rubber from the landfill per lane-kilometre. The findings demonstrate that the CR modification can reduce the continuously estimated required asphalt thickness and extend the theoretical pavement service life under the adopted assumptions, although the thickness reduction is not resolved when standard 2.5 cm construction increments are considered. Additionally, CR modification provides measurable sustainability benefits through the reuse of scrap-tyre rubber.

The growing demand for sustainable pavement materials has driven increased interest in asphalt mixtures incorporating recycled crumb rubber (CR). While CR modification enhances mechanical performance and durability, its often increases initial production costs and energy demand. This study develops an integrated framework that combines machine learning (ML) and economic analysis to identify the optimal balance between performance and cost in CR-modified asphalt overlay mixtures. An experimental dataset of conventional and CR-modified mixtures was used to train and validate multiple ML algorithms, including Random Forest (RF), Gradient Boosting (GB), Artificial Neural Networks (ANNs), and Support Vector Regression (SVR). The RF and ANN models exhibited superior predictive accuracy (R² > 0.98) for key performance indicators such as Marshall stability, tensile strength ratio, rutting resistance, and resilient modulus. A Cost–Performance Index (CPI) integrating life-cycle cost analysis was developed to quantify trade-offs between performance and economic efficiency. Environmental life-cycle assessment indicated net greenhouse gas reductions of approximately 96 kg CO₂-eq per ton of mixture despite higher production-phase emissions. Optimization results indicated that a CR content of approximately 15% and an asphalt binder content of 4.8–5.0% achieve the best performance–cost balance. The study demonstrates that ML-driven optimization provides a powerful, data-based approach for guiding sustainable pavement design and promoting the circular economy in road construction.

The accurate prediction of the mechanical behaviour of silica–epoxy nanocomposites is essential for advancing their application in high-performance industries, including aerospace, automotive, and structural engineering. Conventional experimental characterization methods are often time-consuming and costly, highlighting the need for efficrelianceient computational alternatives. This study proposes a machine learning based on Random Forest Regression to predict the stress–strain behaviour of silica–epoxy nanocomposites with high accuracy. The model employs two independent and physically meaningful input parameters—SiO₂ nanoparticle concentration (wt%) and strain—to predict stress, thereby capturing the true constitutive relationship of the material. The model was trained and validated on an extensive experimental dataset of 7422 observations across five compositions (0–4 wt% SiO₂), obtained from systematic tensile testing following the ASTM D638 standard. Rigorous stratified 10-fold cross-validation confirmed excellent generalization (mean R² = 0.9977 ± 0.0023) with minimal overfitting (training–validation gap < 0.005). The performance of the test set (R² = 0.9948, mean absolute error (MAE) = 0.0404 MPa) surpasses recent literature benchmarks by nearly 5%, establishing state-of-the-art accuracy in nanocomposite property prediction. Error analysis revealed stable prediction accuracy throughout the elastic and plastic regimes (error variance < 0.004 MPa² for strain), with a physically consistent increase in error near failure due to complex damage mechanisms. Feature importance analysis indicated that strain and SiO₂ concentration contributed 78.4% and 21.6%, respectively, to predictive accuracy. This is consistent with constitutive modelling principles, in which deformation state primarily determines stress magnitude, while composition modulates the functional relationship. Mechanical property extraction from experimental curves showed optimal performance at 2–3 wt% SiO₂, yielding balanced enhancements in tensile strength (+1–2%) and failure strain (+36–64%) relative to neat epoxy. The validated framework reduces material development time by 65–80% and cost by 60–75% compared with conventional trial-and-error methods, offering a robust, data-driven tool for the efficient design and optimization of silica–epoxy nanocomposites. A comprehensive discussion of limitations and applicability boundaries ensures the framework’s responsible and reliable deployment in engineering practice.