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.

This study investigates the structural behavior of composite double-web steel beams filled with different types of concrete made from a combination of recycled concrete aggregates and normal aggregates. The research includes both experimental and numerical analyses. Seven specimens were tested under symmetrical two-point loading, all having identical geometric properties: a span length of 1100 mm, flange plates 120 mm wide and 6 mm thick, and web plates 3 mm thick and 188 mm deep. The specimens were divided into two groups, with a control beam without concrete infill. Group one included beams filled with normal concrete in different locations (middle region, two sides, and fully filled), while group two mirrored the same fill locations but used recycled concrete instead. The experimental results showed that using normal concrete improved the ultimate load by 10.19% to 55.30%, with the fully filled beam achieving a maximum increase in ductility of about 568% and a stiffness improvement ranging from 2.6% to 39% compared to the control beam. Beams filled with recycled concrete showed increases in ultimate load from 9.52% to 42.03%, ductility improvements of up to 380%, and stiffness enhancements between 4.5% and 8.03%. Numerical modeling using ABAQUS (2021) showed excellent agreement with the experimental results, with differences in ultimate load and maximum deflection averaging 5.5% and 7.9%, respectively.

In order to better understand the mechanical properties of lightweight cement-based composite concrete (LWC), expanded polystyrene (EPS) beads are used as lightweight aggregate (LWA) in this paper. 50%, 70%, and 90% of EPS foam beads by volume are used to partially replace normal fine aggregate in different EPS concrete compositions. In addition, Ordinary Portland cement (OPC) was substituted with silica fume (SF) in EPS concrete at varying weight percentages of 15%. Nine mixes are made in order to examine the properties of EPS concrete. In the testing program, fresh density, slump, compressive strength, splitting tensile strength, flexural strength, thermal conductivity, and absorption are all determined. Although workability is improved, the mechanical properties of concrete are generally decreased when EPS beads are used. The addition of silica fume (SF) successfully counteracted the mixture’s overall decline in mechanical properties across all the mixtures that have been used. More solid material can be found per square inch of surface area in materials with a higher density, which results in more continuous heat-conduction pathways. In comparison to the control mix, the compressive strength of the polystyrene modified mixes showed a noticeable decline, falling by roughly 62% for P-50%, 69% for P-70%, and 71% for P-90%. In contrast, mixes P-90%-1.2, P-90%-1.4, and P-90%-1.6 reduced absolute strength compared to P-90%; their performance is nonetheless noteworthy because of their extraordinarily high EPS content. Despite having lesser absolute strengths than P-90%, mixes of P-90%-1.2, P-90%-1.4, and P-90%-1.6 nevertheless performed admirably considering their remarkably high EPS content.

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.

Reactive Powder Concrete (RPC) has high performance in properties, so it is desirable in the final years. But it was because the cost of this type of concrete made the world need sustainable reactive powder concrete. A high amount of waste materials resulted from different factories; these materials were left in regions and became the source of dangers. Therefore, this paper presents a new type of reactive powder concrete with waste steel fibers and introduces the investigation of four mixes of RPC containing four volumetric volumes of waste steel fiber: 0%, 0.5%, 1%, and 1.5%. The properties considered in this work are the compressive strength, splitting, flexural strength, density, and absorption at two ages, 7 and 28 days. The main result outcome from this study is that an improvement in compressive, splitting, and flexural strength values with an increase in the percentage of waste fiber was reached, with the highest value of compressive strength at 18.07% compared with the reference mix at age 28 days. While the highest value of splitting and flexural strength reached about 35.16% and 28.26%, respectively. It can be shown that the ratio of increase in tensile strength is more than in compressive strength due to the fiber working as the bridge in prose and delaying the growth of the crack.

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.