## 2 Literature Review
### Data-Driven Artificial Intelligence Methods  
As mining activities deepen and geological conditions become more complex, traditional theoretical analysis and numerical simulation methods have gradually revealed limitations in dealing with high-dimensional, multifactorial, and complex nonlinear problems. These methods often rely on simplified assumptions and ideal conditions, or are highly sensitive to initial input parameters, making them unable to fully capture the diversity and complexity of geological conditions in real environments. This, in turn, affects the accuracy and applicability of their predictions. Furthermore, traditional methods often struggle to fully utilize the rich information contained in field monitoring and experimental data, thereby hindering a comprehensive understanding of stress fields. Unlike traditional methods, the rapidly developing machine-learning techniques in recent years do not rely on explicit mathematical models. Instead, they learn from large volumes of historical data to identify underlying patterns and laws, which allows for predictions on new data [11]. Machine learning has demonstrated significant advantages in handling high-dimensional data, complex nonlinear relationships, and multifactorial interactions [12]. As a result, an increasing number of studies have turned to data-driven machine-learning methods. In earlier studies, commonly used models were primarily machine-learning models such as random forests. For example, the study by [PERSON] et al. employed multiple algorithms to analyze the spatiotemporal distribution patterns of excavation damaged zones (EDZs) during roadway excavation. Their results indicated that the random forest model demonstrated superior predictive performance, with a root-mean-square error (RMSE) of 0.1613 and a mean absolute error (MAE) of 0.1402 [13]. With the advancement in research, scholars have begun incorporating deep-learning techniques to enhance the accuracy of stress prediction. [PERSON] et al. proposed a method based on long short-term memory (LSTM) neural networks and transfer learning, which significantly improved the prediction performance of mining-induced stress on roof rocks under the conditions of missing monitoring data [14]. They found that the random forest model had an R\({}^{2}\) of 0.85, which was higher than the multilayer perceptron (0.76), and that mining operations had a more significant impact on roof stress than geological condition parameters. This indicates that machine-learning methods not only improve the prediction accuracy but also help uncover domain-specific knowledge. [PERSON] et al. developed a finite element convolutional neural network (FE-CNN) framework for stress field analysis. Empirical studies demonstrated that this method performs well in handling complex geometries and material discontinuities, significantly reducing computation time [15]. In 2024, [PERSON] et al. proposed a method based on limited monitoring points and machine learning to realize a holistic perception of the three-dimensional stress state of the roof of a roadway, and installed the developed sensors in an engineering example, and found that the model yielded reasonable results for the derivation of the roof stresses [16]. Due to the powerful performance demonstrated by machine learning (especially deep learning), researchers have directly applied these techniques to areas such as roof stability prediction and mine instability analysis. For example, [PERSON] et al. combined particle swarm optimization (PSO) with artificial neural networks (ANN), and the results showed a significant improvement in prediction accuracy with the PSO-ANN model [17]. [PERSON] et al. used a combination of principal component analysis (PCA), a differential evolution algorithm (DE), and a multi-class support vector machine (SVM) to classify the risks in mined-out areas, finding that the SVM exhibited strong generalization capabilities [1]. [PERSON] et al.