Golden jackal optimization-based regression analysis application on volume expansion estimation of cement pastes with MgO expansive additive

The implementation of massive amounts of fly ash (FA) and MgO expansive additive ( MEA ) is impeded by their limited hydration capacity and the challenges associated with regulating delayed expansion. The current collection of literature lacks sufficient study on the application of learning methods to estimate volume expansion (Ve) of cement paste, specifically when using FA and MEA. The process of designing and confirming methods for the evaluation of Ve involves using a dataset including 170 experimental outcomes obtained from papers. To accomplish this goal, researchers have developed support vector regression (SVR). In the present research, the optimizers known as the Equilibrium Optimization Algorithm (E OA ), Golden jackal optimization algorithms ( GJOA ), and Henry gas solubility optimization ( HGSO ) were chosen for SV R hyperparameters' tuning. The SV RHG, SV R EO and SV R GJ models possess a substantial capacity to precisely forecast the Ve of cement paste containing MAE and FA . For O B J index, the smaller value was attributed to SV RGJ, accounting for 0.0042, almost 50% smaller than SV R EO at 0.0085 and roughly three times slither than SV R HG by 0.0131. The superior performance of the G JO A-optimized SV R model suggests that GJOA is particularly effective in handling the non-linear relationships present in the dataset. This finding has important implications for the design of cement pastes, particularly in optimizing the use of MEA and FA .