## 1 Introduction  
Rock fragmentation plays a pivotal role in engineering disciplines such as mining, civil engineering, and construction [1, 2, 3, 4]. In mining engineering, the rock fragmentation process entails the disintegration of rock mass or ore into smaller fragments using explosives, drilling apparatuses, and thermal stress. Studies have indicated that only approximately 30% of the explosive energy is effectively used for fragmentation [5, 6]. The remaining energy is disseminated into other forms, which leads to adverse effects including back break, airblast, fly rock, and ground vibration [7, 8, 9, 10, 11, 12, 13]. The fragmentation size is the most critical factor during the blasting process, as it directly influences the efficiency of ore extraction,downstream processes, equipment management, and material handling [14; 15]. Achieving an ideal fragmentation size not only enhances mines' productivity but also improves their operational safety. Therefore, a comprehensive understanding of the fragmentation process is essential for making accurate predictions and optimizing outcomes. Predicting the optimum fragmentation size is a challenging task due to the inherent complexity of blasting techniques [16; 17]. To address this, researchers have proposed various approaches to optimize parameters and simplify the problem. For example, [PERSON] developed a model to predict the mean fragmentation size (MFS) based on the properties of the rock mass and explosives used [18]. [PERSON] subsequently refined this model, known as the Kuz-Ram model, by incorporating specific energy and additional rock mass characteristics [19; 20; 21]. Although these empirical models have limitations, they laid the groundwork for subsequent advancements in the field. Recognizing the need for more accurate predictions, researchers developed models that consider the full range of blasted fragment sizes, such as the distribution-free, Swebrec, and Rosin-Rammler functions [14; 22; 23; 24]. Moreover, improvements to the Kuz-Ram model, such as the modified Kuz-Ram, Bergmann, and Riggle models, were proposed to enhance its predictive accuracy [25; 26]. To further improve its practicality, researchers also developed equations for the BRW model to facilitate its real-world application. Recently, researchers have progressively embraced artificial intelligence (AI)-based methods because of the limitations of the empirical models, especially their reliance on labor and costly field blast experiments. AI-based methods have achieved significant success in the geotechnical domain. For instance, AI-based methods have improved the precision of rock fragmentation predictions, pattern recognition, and data analysis [27; 28; 29; 30; 31]. Commonly used AI-based techniques for predicting blast-induced rock fragmentation include artificial neural networks (ANNs), fuzzy logic, genetic algorithms, and ensemble methods. Table 1 provides an overview of the AI-based techniques applied in the prediction of MFS. However, these techniques still need improvements, such as a reduction in their training time and improvement in their prediction accuracy, regardless of the quantity of the dataset. Among these techniques, tree-based algorithms have shown superior performance compared to ANNs, etc., even without extensive fine-tuning [32; 33]. The existing literature reveals that most prior studies have primarily relied on small-scale blasting datasets, which limit models' performance and increase the risk of overfitting. In contrast, AI-based models exhibit significantly improved performance when trained on large-scale datasets, as these enable the models to capture more complex patterns within the data. Moreover, large-scale datasets also improve the models' generalization and allow them to perform better on new data.