Predictive modeling and design optimization of concrete properties reinforced with basalt fiber under harsh environment

Abstract Basalt fiber reinforced concrete (BFRC) has recently attracted increased attention for improving durability, mechanical strength, and chemical resistance concerning harsh environmental conditions. The most noticeable gap is left in being able to predict long-term performance accurately and optimize that performance because of the complexities that arise from the multiscale interactions between fibers, matrix, and environmental stressors. This study, therefore, offers a highly unified and multiscale machine learning framework by pulling together five disaggregated analytical models into a single predictive-optimization pipeline prearranged for basalt fiber reinforced concrete. The physics-augmented graph attention transformer network (P-GATNet) is expected to embed interfacial physics within graph-based message passing to capture load-driven mechanical responses, resulting in highly accurate strength and fracture evolution predictions (e.g., flexural R² ≈ 0.97). The spectral decomposition assisted degradation model uses Hilbert-Huang-based spectral analysis, which then decouples degradation mechanisms for accurately forecasting alkali resistance and damage kinetics with an error of less than 4.5%. The multi-agent physics reinforcement optimizer (MAPRO) jointly optimizes the strength and chemical performance by modeling competing failure mechanisms via cooperative agents. For improved representation of features, the deep morphological encoder with multi-modal fusion (DME-MMF) marries image-derived morphological embeddings with experimental tabular data, thus enhancing the interpretability and accuracy of predictions. Lastly, the transformer-based inverse composite generator enables reverse material design by producing feasible basalt fiber reinforced concrete formulations that satisfy predetermined strength and durability targets at an approximate success rate of 93%. This approach improves predictive fidelity, interpretability, and design in basalt fiber reinforced concrete.