Data-driven framework with graphical user interface for predicting flexural behavior of FRCM strengthened RC beams

In this study, the flexural capacity of reinforced concrete (RC) beams strengthened with fiber-reinforced cementitious matrices (FRCM) was computed using machine learning (ML) algorithms including: (i) adaptive neuro-fuzzy inference system (ANFIS), (ii) artificial neural network (ANN), and (iii) extreme gradient boosting (XGBoost). A total of 198 pertinent experimental datasets were compiled and included six types of FRCM composites (PBO, carbon, glass, basalt, coated carbon, and combined glass and carbon). The considered input parameters comprise the beam cross-sectional details, area of tensile and compressive steel reinforcement, mechanical properties of FRCM composite, and concrete compressive strength. To assess the reliability of ML models, four existing analytical models and one established standard guideline were used for comparison. Moreover, six statistical metrics were employed, along with an overfitting analysis, to determine the best-fitting model. Graphical fitting of the optimal model was depicted using the Taylor diagram, violin plot, as well as multi-panel histogram plot. Based on both graphical and statistical metrics, the XGBoost model attained the highest precision compared to all analytical and ML-based models. The correlation coefficient and MAPE of the XGBoost model were 0.9977% and 2.98%, respectively. To interpret the influence of individual parameters on the flexural strength of the FRCM-strengthened RC beams, a feature importance plot based on SHAP explanatory theory was deployed. Ultimately, a user-friendly graphical interface was developed and made accessible to aid practicing engineers in estimating the flexural strength of FRCM-strengthened RC beams, offering an effective alternative to complex design procedures.