This research suggested natural hemp fiber-reinforced ropes (FRR) polymer usage to reinforce recycled aggregate square concrete columns that contain fired-clay solid brick aggregates in order to reduce the high costs associated with synthetic fiber-reinforced polymers (FRPs). A total of 24 square columns of concrete were fabricated to conduct this study. The samples were tested under a monotonic axial compression load. The variables of interest were the strength of unconfined concrete and the number of FRR layers. According to the results, the strengthened specimens demonstrated an increased compressive strength and ductility. Notably, the specimens with the smallest unconfined strength demonstrated the largest improvement in compressive strength and ductility. Particularly, the compressive strength and strain were enhanced by up to 181% and 564%, respectively. In order to predict the ultimate confined compressive stress and strain, this study investigated a number of analytical stress–strain models. A comparison of experimental and theoretical findings deduced that only a limited number of strength models resulted in close predictions, whereas an even larger scatter was observed for strain prediction. Machine learning was employed by using neural networks to predict the compressive strength. A dataset comprising 142 specimens strengthened with hemp FRP was extracted from the literature. The neural network was trained on the extracted dataset, and its performance was evaluated for the experimental results of this study, which demonstrated a close agreement.

This study investigates the enhancement of axial and shear strength in brick masonry walls reinforced with steel and fiberglass meshes. The novelty of this study lies in its thorough evaluation of various reinforcement types and their influence on both axial and shear strength, offering valuable insights to enhance the performance of brick masonry structures. By using steel and fiberglass meshes for reinforcement, the study promotes the use of durable materials that can extend the lifespan of brick masonry structures, reducing the need for frequent repairs and replacements. The findings reveal that double-layer steel mesh delivers the highest strength, effectively reducing brittleness and improving deformation capacity in both single- and double-brick walls. Specifically, single-brick walls exhibited increases in compressive strength of 38.8% with single-layer steel mesh, 31.2% with fiberglass mesh, and 19.7% with plaster. In contrast, double-brick walls showed enhancements of 73.6% with double-layer steel mesh and 43.5% with fiberglass mesh. For shear strength, single-brick walls improved by 115.1% with single-layer steel mesh, 91.3% with fiberglass mesh, and 42.1% with plaster, while double-brick walls experienced increases of 162.7% with double-layer steel mesh and 132.5% with fiberglass mesh. Additionally, Abaqus modeling under axial and diagonal compression closely matched experimental results, revealing less than a 10% discrepancy across all reinforcement types.

This study investigates the axial compressive behaviour of rectangular concrete specimens confined with low-cost Glass Chopped Strand Mat (GCSM) sheets. While the GCSM has been explored in other contexts, this is the first study specifically investigating its effects on rectangular concrete specimens. A total of 24 specimens were tested, grouped by different unconfined concrete strengths. Each group included unconfined specimens and GCSM-confined specimens wrapped with 2, 3, and 4 layers. The results demonstrate that GCSM confinement significantly enhances both compressive strength and ductility, particularly in low-strength concrete, where normalized gains in strength and strain exceeded 50% and 160%, respectively. The post-peak modulus decreased with increasing confinement ratio, indicating improved energy dissipation and delayed failure. Additionally, experimental elastic modulus values showed good agreement with ACI 318 predictions. Analytical models were developed to predict peak strength, peak strain, and post-peak modulus as functions of confinement pressure, achieving excellent correlation with experimental data (R² > 0.98).

Using waste materials in the manufacture of concrete has many environmental advantages. However, it can be difficult to estimate structural performance, especially when beams are reinforced with fiber-reinforced polymers (FRP). In order to provide a data-driven approach to sustainable structural design, this work explores the use of machine learning (ML) approaches to forecast the flexural strength of FRP-strengthened waste aggregate concrete beams. A total number of 92 experimental datasets were used to develop and assess four ML algorithms: Random Forest (RF), Decision Tree (DT), Neural Network (NN), and Extreme Gradient Boosting (XGBoost). Regression plots, Taylor diagrams, statistical measures (R2R^2R2, RMSE, MAE, MSE), and explainable AI (XAI) tools, including SHAP, LIME, and partial dependence plots (PDPs), were used to evaluate the model’s performance. RF outperformed NN in terms of predictive accuracy, while XGBoost exhibited similar performance to RF. The most significant predictors, according to a SHAP analysis, were beam length and fiber length, with the lower followed by steel tensile strength, fiber width, and concrete compressive strength. LIME offered local interpretability for individual predictions, but PDPs demonstrated optimal parameter ranges and a nonlinear feature strength relationship. The findings provide engineers with a strong decision-support tool for designing green infrastructure, since they show that ensemble-based models can accurately represent the intricate, nonlinear dynamics controlling flexural behavior in sustainable FRP-strengthened waste aggregate concrete beams.

Masonry buildings have shaped construction history since about 6500 BCE. They offer durability, strength, and cost effectiveness, especially in developing countries. Yet assessing compressive strength during construction remains challenging due to the constituent materials soil, cement, and stone, complicating standardization worldwide. In the present study, an innovative model based on a machine learning algorithm is put forth to predict the compressive strengths of prisms. Some important factors considered as input to the algorithm based on traditional methods are the brick and mortar strengths, prism geometry, mortar bed thickness, and empirically derived height-to-thickness (t) (h/t) ratios. Three different ANN algorithms are coded and trained on the input data, and they are based on the Levenberg–Marquardt algorithm, the resilient backpropagation algorithm, and the conjugate gradient algorithm. The optimal ANN model trained using the conjugate gradient Polak–Ribière algorithm (traincgp) achieves superior performance, with R² = 0.9881, R² = 0.9927, RMSE = 0.9914 MPa, MAE = 0.6039 MPa, MAPE = 20.9141%, VAF = 0.9881, and WI = 0.9970. Sensitivity analysis shows the height-to-thickness (h/t) ratio is the dominant influence on compressive strength, consistent with structural mechanics. The primary contributions are the systematically curated, richly parameterized dataset and its use to produce robust, physically interpretable predictions with established ANN methods.