Parametric Evaluation and Prediction of Compressive Capacity of FRP Rebar-Reinforced Concrete Columns with Seawater and Sea Sand
This study investigates the compressive performance of fiber-reinforced polymer (FRP) rebar-reinforced Seawater and Sea Sand Concrete (SSC) columns through an integrated approach combining finite element analysis, theoretical derivation, and machine learning. Finite element models were developed to quantify the influence of key parameters on the ultimate bearing capacity and lateral deflection. The results indicate that the compressive capacity decreases significantly with increasing eccentricity and slenderness ratio. Columns reinforced with steel rebars demonstrated superior load-bearing and anti-lateral displacement capabilities compared to their FRP-reinforced counterparts. A theoretical formula for predicting the compressive capacity was derived; however, it systematically overpredicted the experimental measurements by approximately 36%. To develop data-driven predictive models for the ultimate load capacity of FRP–SSC columns, four machine learning models, backpropagation neural network (BPNN), bootstrap aggregating BPNN (Bagging-BP), genetic algorithm-optimized BPNN (GA-BP), and gradient boosting regression trees (GBRT), were employed. Using sectional dimension, concrete strength, reinforcement parameters, eccentricity, and slenderness ratio as inputs, the validation sets of the models achieved R-values of 0.942, 0.918, 0.933, and 0.990, respectively. Feature importance analysis based on SHAP identified eccentricity as the most influential parameter. Results from this work can help to understand the behavior of FRP–SSC columns under compression.