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• The behavior of post-tensioned steel–concrete composite girders is numerically studied. • Finite element model was developed and validated using experimental and analytical results. • A parametric study was conducted to study the effects of several factors. • The model helped better understand the effects of these parameters on the monotonic behavior of the tested beams. The monotonic behavior of externally post-tensioned steel–concrete composite girders was numerically studied in this paper. A three-dimensional numerical model was developed and validated using experimental test results that were conducted by the authors and using an existing analytical model. A parametric study using this validated numerical model was performed to investigate the effects of various parameters on the monotonic performance of composite girders strengthened with external post-tensioned tendons. The parameters investigated include variations in the degree of shear connection, layout and diameter of shear connectors, the initial post-tensioning force, the depth of the steel beam, the eccentricity of the tendons, the compressive strength of concrete, and the shear capacity of the studs. The numerical model provided a better understanding of the effect of these parameters on the behavior of the strengthened beams. The results of the parametric study show that as the degree of shear connection decreased, the stud strains increased and the slippage between the concrete deck and steel beam increased. As the full degree of shear connection was reduced to 80%, 60%, and 40%, the stud strains were increased by 20%, 46%, and 94%, respectively. Also, as the shear connection degree decreases, its effect on the slippage behavior increases. As the degree of shear connection was reduced to 80%, 60%, and 40%, the slippage values were increased by 23%, 48%, and 102%, respectively. The study also shows that, for the same degree of shear connection, beams with one row of shear studs had up to 10% higher flexural capacity than beams with two rows of studs. A 100% increase in the stud diameter caused a 28% reduction in the ultimate load capacity and a 160% increase in the maximum slippage. The higher the post-tensioning force, the higher the ultimate load capacity and the lower the tensile strains in the steel beam. A 118% increase in the post-tensioning force resulted in a 9% increase in the ultimate load capacity and a 13% reduction in the tensile strains. Increasing the depth of the steel beam by 40% resulted in a reduction in the tensile strains at midspan by 20%. The lowest midspan tensile strains were obtained from the combination of increasing the depth of the steel beam and tendon eccentricity. [ABSTRACT FROM AUTHOR]

• E-waste elimination. • Mechanical properties concrete incorporating E-waste. • Artificial Intelligence (AI) • ANN. • Environmental hazard. The appropriate disposal of electronic waste (E-waste) is becoming a serious concern on a global scale. The purpose of present work is to establish a link between the mix design factors and mechanical strength using the metaheuristic based artificial intelligence (AI) Technique known as gene-expression-programming (GEP). The developed dataset includes several input variables i.e., the percentage of e-waste partial substitute, water to cement ratio, specimen age, water absorption and specific gravities of the aggregates, while the compressive strength (CS), flexural strength (FS) and tensile strength (STS) are used as predictive outcome. The established models were assessed using the root mean square error (RMSE), mean absolute error (MAE), objective function, and performance index as well as the regression measure known as the coefficient of correlation (R2). All strength models showed a significant correlation (R2 = 0.94), with the minimum statistical errors (MAE 2.04, RMSE 2.54), (MAE 0.36, RMSE 0.47), and (MAE 0.43, RMSE 0.54) for CS, FS and STS respectively. Furthermore, the parametric and sensitivity analyses were considered for analyzing impact of particular input variables on the performance of outcome. The established machine learning based metaheuristic models can be utilized confidently to use e-waste concrete in a variety of construction purposes. [ABSTRACT FROM AUTHOR]

• Four ensemble M−L algorithms were employed to predict the C-S of C-M after acid attack. • A dataset of 234 points with eight input parameters was used for M−L modeling. • SHAP analysis determined the significance and interaction of input features. • Accurate predictions were made for the C-S of C-M after acid attack using M−L methods. • Bagging and random forest methods yielded more accurate results than gradient boosting and AdaBoost. This research aimed to build estimation models for the compressive strength (C-S) of cement mortar containing eggshell and glass powder after the acid attack using machine learning algorithms. A lab test data comprising 234 data points with 8 input factors was utilised for modelling. Four ensemble machine learning techniques, including gradient boosting, AdaBoost, random forest, and bagging, were employed to achieve the research's goals. In addition, to examine the influence and correlation of input factors, a SHapley Additive exExplanations (SHAP) analysis was conducted. The built estimation models well agreed with the lab test results based on R2 and the variance between actual and model estimated results (errors). Random forest and bagging exhibited superior prediction performance, with R2 of 0.982 and 0.983, respectively, than gradient boosting and AdaBoost, with R2 of 0.969 and 0.977, respectively. The comparative analysis of statistical measures also indicated superior accuracy of random forest and bagging, with mean absolute percentage error (MAPE) of 2.40%, than gradient boosting and AdaBoost, with MAPE of 2.90% and 2.60%, respectively. SHAP analysis exhibited that the highly influential factor for the acid resistance of glass and eggshell-based mortar was the 90-day C-S of the sample, followed by the quantity of glass powder, eggshell powder, sand, cement, water, superplasticizer, and silica fume. [ABSTRACT FROM AUTHOR]