# Random Forest-Based Stability Prediction Modeling of Closed Wall for Goaf
###### Abstract
This paper selects the random forest model precisely because of its complementarity with the finite element method--the latter provides physics-driven, refined simulation data, while the former achieves efficient prediction through a data-driven approach. The combination of these two can provide a multi-scale solution for the stability assessment of retaining walls. The scholars mentioned above have conducted extensive research on roofing disasters and the stability of the mining zone, yielding significant results. However, there are still many challenges in practical application. Most existing prediction models are complex to build, require a large amount of data to be collected, and are difficult to deploy in practice, and many theoretical analyses and numerical simulations rely on simplified assumptions, often overlooking the complexities of the actual environment, such as material non-homogeneity and variations in the ambient temperature and humidity. Additionally, experimental conditions have some limitations. The experimental research is often limited by experimental conditions and equipment, making it difficult to fully simulate actual working conditions. In particular, large-scale explosion simulation experiments are costly and risky, which further constrains their applicability. Moreover, there is a lack of long-term performance evaluation, as most research focuses on the effects of transient impact and short-term loading, and there is a lack of systematic research on the environmental erosion and fatigue damage suffered by closed walls in the course of long-term use. The shock wave generated by a roof fall in a mined-out area seriously affects the quarry stability and personnel safety. To investigate the effects of these shock waves on sealing protection requirements, data on the shock wave sizes generated by roof falls in various mined-out areas and the mechanical parameters of the closed walls were systematically collected. An RF algorithm was employed to fit the influence of the mechanical parameters of the closed walls and the shock wave size on the construction area under their coupled effects. The safety threshold values for the mechanical parameters of the closed walls were accurately predicted under the pressure of shock waves generated by roof falls in different mined-out areas, thereby providing technical guidance for similar engineering practices. In this study, we propose the following four research hypotheses: (1) the mechanical parameters and shock wave velocity jointly influence the stability of confined walls; (2) the random forest model can effectively capture nonlinear relationships; (3) hyperparameter optimization can significantly improve the model accuracy; (4) the model possesses practical engineering applicability. Based on these hypotheses, a closed wall damage prediction model based on an RF algorithm is proposed, and the hyperparameters are optimized using a grid search to enhance the model prediction performance. A random forest algorithm model is trained for prediction, and the trained machine learning model is further tuned with hyperparameters. The contributions of this research are as follows: (1) the development of an efficient damage prediction model for confined walls; (2) the introduction of a model hyperparameter optimization algorithm combining a grid search method; (3) the proposition of an efficient prediction model based on the random forest algorithm for predicting the extent of damage to confined walls in engineering instances; (4) experimental validation of the proposed model based on actual working conditions.