Novel approaches to predict the Marshall parameters of basalt fiber asphalt concrete

•This study pioneers the use of XGB model coupled with SFO and AO for predicting the Marshall parameters of BFAC.•The predictive ability of the trained model was thoroughly tested, demonstrating its stability and consistency.•The predictive model showcased high accuracy, with R-values of 0.976 and 0.909 for Marshall Stability and Flow, respectively.•The study also conducted design optimization for BFAC samples using the SFO algorithm through diferent scenarios. Experimental approach for evaluating the Marshall Stability (MS) and Marshall Flow (MF) of basalt fiber asphalt concrete (BFAC) is time-consuming and expensive. This study employed the Extreme Gradient Boost (XGB) algorithm in conjunction with two novel optimization algorithms, namely Sailfish Optimizer (SFO) and Aquila Optimizer (AO), to construct novel and enhanced prediction models for MS and MF of BFAC. Two databases were compiled from 18 experimental investigations with 128 and 89 experimental samples, respectively for MS and MF. Ten input parameters covering the mixture components were considered for MS and MF modeling. In addition, cross-validation was applied to evaluate the generalizability of the trained XGB model. The obtained findings demonstrated that the XGB model has an excellent and consistent predictive capacity, thereby demonstrating the model's stability in predicting MS and MF. In addition, the SFO optimization algorithm was applied for a constrained design optimization problem regarding the composition mixture for BFAC. The results were discussed considering existing design standards.