The construction industry has witnessed a substantial increase in the demand for eco-friendly and sustainable materials. Eco-friendly concrete containing Ground Granulated Blast Furnace Slag (GGBFS) and Recycled Coarse Aggregate (RCA) is such a material, which can contribute to a reduction in waste and promote environmental sustainability. Compressive strength is a crucial parameter in evaluating the performance of concrete. However, predicting the compressive strength of concrete containing GGBFS and RCA can be challenging. This study presents a novel XGBoost (eXtreme Gradient Boosting) prediction model for the compressive strength of eco-friendly concrete containing GGBFS and RCA, optimized using Bayesian optimization (BO). The model was trained on a comprehensive dataset consisting of several mix design parameters. The performance of the optimized XGBoost model was assessed using multiple evaluation metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). These metrics were calculated for both training and testing datasets to evaluate the model’s accuracy and generalization capabilities. The results demonstrated that the optimized XGBoost model outperformed other state-of-the-art machine learning models, such as Support Vector Regression (SVR), and K-nearest neighbors algorithm (KNN), in predicting the compressive strength of eco-friendly concrete containing GGBFS and RCA. An analysis using Partial Dependence Plots (PDP) was carried out to discern the influence of distinct input features on the compressive strength prediction. This PDP analysis highlighted the water-to-binder ratio, the age of the concrete, and the percentage of GGBFS used, as significant factors impacting the compressive strength of the eco-friendly concrete.

Reinforced concrete (RC) flat slabs are widely employed in modern construction, and accurately predicting their load-carrying capacity is crucial for ensuring safety and reliability. Existing design methods and empirical equations still exhibit discrepancies in determining the ultimate load capacity of flat slabs. This study aims to develop a robust machine learning model, specifically the M5P model tree, for predicting the punching shear capacity of a RC flat slab without shear reinforcement. A comprehensive dataset of 482 experimentally tested flat slabs without shear reinforcement was gathered through an extensive literature review and utilized for the development of the M5P model. The model takes into account influential parameters, such as slab thickness, longitudinal reinforcement ratios, and concrete strength. The performance of the proposed M5P model was compared with existing design codes and other empirical models. The comparison highlights that the developed M5P model tree provides a more accurate and reliable prediction of the punching shear capacity of RC flat slabs. This study contributes to the advancement of structural engineering knowledge and has the potential to improve the design and safety assessment of concrete flat slab structures.

The precast concrete roofing slab produced from the local factory were below the Iraqi standard requirement (NO 1107-1988).This study has been made to improve the production of precast concrete slabs by using(propylene – ethylene copolymer grid )which is a waste material produced from the base of polymer boxes . The depended mix of the precast concrete slab in the local factory did not use the correct proportion ,tested mixes were redesigned and prepared to achieve the limited requirements . Propylene – ethylene copolymer grid was used to have better results, less costs, with protection of the environment from pollution ,where there is difficulty to remove it even when it is fully exposed to severe weather conditions .the manufactured slabs had dimensions of (800×800×40)mm and testing program extended to 60 days of age and comprised ,breaking loads, water absorption ,and impact resistance of concrete. The total number of manufactured samples was (55 slabs). The test results indicated that the weigh ted mix proportions ((1:1.5:2.45) cement :sand : gravel ) with w/c=0.41 , superplasticizer(0.75 liter/100 kg of cement )and propylene –ethylene copolymer grid with a dimension (600×600)mm enhances the mechanical properties of concrete . It increased the breaking load by 81 % compared to ordinary production. while the impact resistance increased to 26 blows compared to 3 blows for the ordinary production. However, the polymer grid decreased the width and depth of cracks and maintained the integrity of the slabs after failure .

Solid waste has always been an undesirable by product of industrialized societies’ operations. The global re- evaluation of the methods used to recycle and use Solid waste has always been an undesirable by product of industrialized societies’ operations. The global re- evaluation of the methods used to recycle and use