## 1 Introduction
Although multiple linear regression (MLR) and logistic regression (LR) have been proposed, an accurate simulation is still challenging mainly due to the curse of dimensionality and co-linearity susceptibility [18, 19, 20]. Therefore, this study aimed to activate WGP using chemical activators and an accurate machine learning (ML) model to predict the FS and ASR expansion of WGP mortars. Recently, the performance of cementitious materials has been broadly studied using artificial intelligence (AI) techniques. ML models exhibit an excellent generalisation ability and perfect prediction accuracy in coping with nonlinear tasks [21,22]. The artificial neural network (ANN), support vector regression (SVR), and random forest (RF) are three widely used ML models that are effective for employing various construction materials [23,24]. The first two models are standalone, while the latter (RF) is an ensemble model that exhibits less probability of generating overfitting problems. RF also has better tolerance than standalone ML models when outliers or noise exist in the data set [25,26]. Furthermore, the blessing of dimensionality and the central limit theorem partly contribute to the success of the ensemble models that outperform the individual models. Therefore, the performance of the RF model is expectedly higher than individual models in solving high dimensionality problems [27; 28]. As a result, the RF model was chosen for data prediction. The accuracy of the RF model depends mainly on two hyperparameters: the total number of trees (\(numTree\)) and the minimum sample number of a leaf node (\(minNumLeaf\)) [29]. The \(numTree\) has a remarkable effect on the model's ability, and the \(minNumLeaf\) controls the splitting condition and determines the relationship between various decision trees. Nevertheless, the process of determining the optimal hyperparameters is time consuming and thus inadequate through the traditional selection method, and if time was not the issue, they would work as well [25]. Recently, optimisation algorithms are preferable since they can automatically seek optimal hyperparameters via iterations. Commonly used metaheuristic algorithms include particle swarm optimisation (PSO) [30], genetic algorithm (GA) [31], and firefly algorithm (FA) [32], which are all computationally intensive. The beetle antennae search (BAS) algorithm uses only one beetle to search the optimal hyperparameters instead of the beetle swarm so that it converges fast and significantly reduces calculation time [33; 34]. However, BAS is easily trapped into a local optimum as indicated by previous researchers [35; 36]. To reduce the possibility of the local optimum, the BAS algorithm with a changeable step size was proposed for the RF hyperparameter adjustment in this study. This study investigated the impact of the mechanical, chemical, and mechanical-chemical (combined) activation on the FS and ASR expansion of mortars containing WGP. The sand was replaced with WGP with mean particle sizes of 75 \(\upmu\)m and 300 \(\upmu\)m. The variables were the replacement ratio, the WGP size, and the content and category of chemical activators (sodium sulphate, calcium hydroxide, and sodium hydroxide). A microscopic test was conducted to investigate the activation effect. In total, 549 samples were prepared for the FS test, and 183 samples were produced for the expansion test. The experimental results are presented as the data set to train the highly accurate BAS-RF model. The baseline models (LR and MLR) were also adopted to compare with BAS-RF. In addition, the sensitivity analysis (SA) was performed on the FS and ASR expansion data sets for a ranking of the input variables.