Abdelrahman Abushanab

Steel corrosion poses a serious threat to the structural performance of reinforced concrete (RC) structures. Thus, this study evaluates the flexural capacity of RC beams through machine learning (ML)-based techniques with six parameters used as input features: beam width, beam effective depth, concrete compressive strength, reinforcement ratio, reinforcement yield strength, and corrosion level. Four single and ensemble ML models are evaluated; namely, decision tree, support vector machine, adaptive boosting, and gradient boosting. Hyperparameters of each model were optimized using grid search and K-fold cross-validation with root mean squared error used as the performance index. The predictive performance of each model was assessed using four statistical performance metrics. The analysis results demonstrated that the decision tree model exhibited overfitting and limited generalization ability. The adaptive boosting model also had a slight overfitting issue. In addition, the support vector machine reported comparable accuracy to that of adaptive boosting. Conversely, the proposed gradient boosting ensemble model achieved the best performance with strong generalization ability, as indicated by its lowest mean absolute error of 2.78 kN.m, mean absolute percent error of 13.40%, and root mean squared error of 3.56 kN.m, and the highest coefficient of determination of 97.30% on the test dataset. The optimized gradient boosting model has been deployed into a graphical user interface, allowing for practical implementation of the model and enabling fast, efficient, and intelligent prediction of the flexural capacity of corroded RC beams.

Eco-friendly concrete is the most recommended sustainable option to reduce the excessive diminution of concrete natural resources and the high generation of greenhouse gas emissions. Therefore, the present study explores the feasibility of employing treated wastewater (TWW), recycled concrete aggregates (RCA), and fly ash (FA) in concrete mixes. A total of 4 reinforced concrete (RC) beams were investigated under 4-point bending setup with three parameters investigated; namely, mixing water (fresh water and TWW), coarse aggregates (gabbro and RCA), and cementitious binders (cement and FA). The experimental results revealed that the beams' ductility was decreased by 9% and 16% with TWW and RCA, respectively. Moreover, the flexural performance of the TWW-RC beams was decreased by 14%, whereas RCA-RC beams showed comparable flexural strength to its counterpart with natural aggregates. In addition, the utilization of FA at a 20% replacement ratio enhanced the flexural strength by 5% to 7%.