Abstract
The research gap addressed in this study is the lack of a transparent and quantitative evaluation of the governing parameters influencing deflection behavior in reinforced concrete (RC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The objective of this study is to identify and quantify the relative importance of the key parameters controlling deflection in GFRP-reinforced RC beams, which exhibit fundamentally different behavior compared to steel-reinforced beams due to the linear-elastic response of GFRP bars until rupture. To achieve this objective, the method integrates explainable artificial intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP), Pearson correlation heatmap, scatter plot analysis, and sensitivity analysis-with experimental structural data obtained from beams with three different concrete strength classes. The main contribution of this study is the quantitative ranking and interpretation of the governing parameters affecting deflection behavior through a transparent and data-driven framework. Key parameters-including elastic modulus (Ec), compressive strength (fck), creep coefficient (φ), failure moment (Mexp), effective moment of inertia (Ieff), and applied load (P)-were evaluated. The results consistently indicate that stiffness- and capacity-related parameters dominate the deflection response. Sensitivity analysis reveals that the failure moment (Mexp) is the most influential parameter, contributing approximately 23% of the total relative influence on deflection, followed by compressive strength (fck) and cracking-related parameters. Pearson correlation heatmap and scatter plot analyses further confirm strong relationships between deflection and Ec, fck, φ, and Ieff. The proposed framework improves the interpretability of deflection prediction in GFRP-reinforced RC beams and provides a transparent basis for serviceability-based structural design and performance-oriented assessment.