This study investigates the time-dependent compressive behavior of seawater sea-sand concrete (SSC) and establishes intelligent prediction models for its evaluation. SSC samples with three strength grades (C30, C40, and C60) were fabricated and tested at curing ages of up to 720 d, accompanied by microstructural characterization. A database containing 344 compressive-strength records was compiled from the present experiments and published literature. Four models were developed and compared, including a back-propagation (BP) neural network, a genetic algorithm-optimized BP (GA-BP) network, a Bagging-BP ensemble model, and a gradient boosting regression tree (GBRT) model. Model interpretability was further examined using SHapley Additive exPlanations (SHAP). The results indicate that SSC exhibits rapid early-age strength gain, while moderate fluctuations may occur during middle and later curing periods. Strength–elastic modulus relationships were established for SSC and showed satisfactory fitting accuracy, with R² values exceeding 0.80. Among the investigated models, GA-BP demonstrated favorable predictive performance under the adopted data split, achieving the lowest root mean square error with R = 0.9413. SHAP interpretation highlighted curing duration and water-to-cement ratio as the dominant factors governing compressive strength. Overall, the proposed GA-BP model can serve as a useful reference for SSC strength prediction and mix-design optimization within the current dataset.

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.