# Random Forest-Based Stability Prediction Modeling of Closed Wall for Goaf
###### Abstract
[13; 19] used numerical simulation to study the propagation law and pressure distribution of shock waves in the bends of a roadway and found that the decay of peak overpressure with distance did not obey the exponential law when air shock waves passed through the bends of the roadway. Utilizing the discrete element method (DEM), [PERSON] et al. [20] investigated the dynamic response of curved masonry structures under blast loading and the effects of the shear expansion angle and tensile strength on the dynamic structural response of masonry. [PERSON] and [PERSON] [21] developed a resistance model and single-degree-of-freedom (SDOF) computational method for unidirectional hollow concrete block-filled brick walls by simulating an unreinforced masonry wall, which can predict the response of this masonry wall under different types of out-of-face static loads. [PERSON] et al. [22] investigated the damage of the roof impact wave on an ore column using theoretical modeling. [PERSON] et al. [23] proposed a simulation model of roof fall in an underground mined-out area based on lattice Boltzmann, which provides a new approach for predicting the disaster of roof fall in mined-out areas. [PERSON] et al. [24; 25; 26] used mathematical methods to carry out a study of stability prediction for the stability of the roof plate of a mined-out area. [PERSON] et al. [27] used the numerical software (ANSYS Fluent 2023 R1) of fluid dynamics to simulate and analyze a roof slab fall in a mining goat area. [PERSON] et al. [28; 29] established a coupled roof fall/air impact model to study the influence of the roof height on the air velocity of the quarry and the roadway when the roof falls. [PERSON] et al. [30; 31; 32] investigated the effects of downhole shock waves on different shapes of wave-retarding walls through experiments and numerical simulations. [PERSON] et al. [33] investigated the effect of different-shaped geometric barriers on mitigating shock waves and their numerical prediction methods using similar simulation experiments and numerical simulations. Furthermore, recent research has been leaning towards using techniques that combine AI-driven approaches and multi-physics coupling for prediction, yielding many promising results. [PERSON] et al. [34] utilized a multi-physics coupling prediction model to develop a fluid-solid coupling model based on asymmetric rock strata structures, considering dynamic geological responses (Rock Mechanics and Rock Engineering), achieving a 38% reduction in the prediction error compared to traditional models. [PERSON], [PERSON], et al. [35] proposed a cross-mining area transfer learning framework in \"Tunnelling and Underground Space Technology\" using an AI-driven approach to address small sample problems (achieving a 90% accuracy with only 150 data points); BYD's mining AI platform employs multi-modal data fusion to integrate microseismic, InSAR, and laser scanning data to predict collapse risks in real time (measured response time < 12 s). [PERSON] et al. [36] proposed Physics-Informed Neural Networks (PINNs) using a physics mechanism and data-combined modeling approach. By constructing a PINN model with constitutive constraints for rock mass damage, they achieved a 24% reduction in error compared to purely data-driven methods. These latest approaches have greatly advanced the research in related fields. Against this backdrop, machine learning methods offer a novel approach to overcome the limitations of numerical simulations. The random forest (RF) algorithm, through its ensemble learning strategy, can efficiently process multi-source heterogeneous data and capture nonlinear relationships between variables. By inputting finite element simulation data as a training set into the RF model, rapid surrogate predictions of the structural response can be achieved. Furthermore, RF models exhibit greater robustness in parameter sensitivity analysis, making them particularly suitable for mining scenarios with complex geological conditions and sparse monitoring data.