Prediction of Impact Resistance of Nano-SiO2 and Hybrid Fiber Modified Geopolymer Gel Concrete in Marine Wet–Thermal and Chloride Salt Environment

The oceanic wet-thermal and chloride salt environment creates extremely harsh service conditions for marine infrastructures. As a green construction material, geopolymer concrete has a promising application prospect in marine engineering due to its excellent durability. The impact resistance of geopolymer concrete subjected to wet-thermal and chloride salt environment is of great significance for the durability and quality of marine engineering structures. This study uses nano-SiO₂ (NS) and hybrid fibers (HF) to enhance the impact resistance of geopolymer gel concrete (GPC). Radial basis function (RBF) and back-propagation (BP) composite neural networks are used to predict the impact resistance of NS and HF-reinforced geopolymer gel concrete (NSHFGPC). The impact resistance of NSHFGPC specimens is characterized by two indicators: the cumulative number of repeated impact blows required to initiate the first visible crack (<i>N</i>₁) and the cumulative number of impact blows corresponding to ultimate failure (<i>N</i>₂). To evaluate the durability of NSHFGPC under oceanic conditions, specimens were exposed to a simulated marine environment within a simulation test chamber for 60 days prior to impact testing. The 60-day duration was selected to achieve a sufficient level of chloride penetration and matrix aging. Based on the resulting experimental database, an RBF-BP neural network was constructed to predict the material's impact resistance. In this study, grid search and K-fold cross-validation were employed to select the optimal hyperparameters. Compared to standalone RBF and BP models, the RBF-BP network demonstrated superior performance, achieving <i>R</i>² values of 0.900 and 0.922. These results represent improvements of 20.18% and 11.18% over the standalone RBF model, respectively. Consequently, the RBF-BP algorithm serves as an experimental tool for predicting NSHFGPC impact resistance and guiding future mix design optimization.