GWO-Optimized BPNN for Abrasion Resistance Prediction of Nano-SiO₂ and Hybrid Fiber Reinforced Geopolymer Gel Concrete

Geopolymer gel concrete (GPC) is a kind of environmentally friendly concrete, which has become a potential alternative material to replace ordinary concrete. Traditional mix design of GPC is carried out under experimental conditions, which is time-consuming and labor-intensive. Geopolymer concrete (GPC) is intended for use in hydraulic structures, which are often exposed to water environments. Water flow exerts significant abrasion and erosion on these structures. If the abrasion resistance (AR) of the material is poor, the service life and service quality of hydraulic structures will be substantially reduced under the action of water flow. Therefore, AR is a key performance indicator for GPC in hydraulic engineering applications. This abrasion resistance can be enhanced by using fibers (for example, steel fibers, polyvinyl alcohol (PVA) fibers, and basalt fibers) and nanomaterials. Furthermore, there is a complex nonlinear relationship between the proportions of fibers and nanoparticles added and the properties of GPC. In this study, the circular ring test method and the underwater steel ball test method were conducted to investigate the AR of nano-SiO₂ (NS) and hybrid fiber (NHF) reinforced geopolymer gel concrete (NHF-GPC). A backpropagation (BP) neural network (BPNN) model optimized by the Grey Wolf Optimizer (GWO) (GWO-BPNN) is established to predict the abrasion resistance strength (ARS) and the abrasion rate of NHF-GPC based on the circular ring test method. In addition, the ARS, abrasion rate, and average abrasion depth (AAD) based on the underwater steel ball test method were also predicted. The results indicate that the GWO-BPNN model demonstrates superior performance over the standard BPNN, exhibiting higher prediction accuracy, better fitting performance, and faster convergence speed. Specifically, for the circular ring test method abrasion rate prediction, GWO-BPNN reduced the root mean square error (RMSE) by 30.3% and lowered the mean absolute percentage error (MAPE) to 8.4%. The GWO-BPNN model established in this study can provide efficient and reliable theoretical support for the optimization of the NHF-GPC mix design.