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SSA-ELM prediction model of roadside support stress in no-pillar mining based on machine learning

artificial-intelligence
computer-education
data-mining-and-machine-learning
scientific-computing-and-simulation
neural-networks

Abstract

To address the complex issue of stress distribution in roadside walls during pillarless mining, this study investigates the stress distribution characteristics and intelligent prediction methods of roadside walls, taking the 8104 working face of Nianyan Coal Mine as the engineering background. The research methods adopted include constructing a dataset with 26 schemes through FLAC3D numerical simulation, designing an SSA-ELM stress prediction model based on the Sparrow Search Algorithm (SSA) optimized Extreme Learning Machine (ELM), and analyzing the stress distribution and evolution laws. The results show that the stress distribution of the roadside wall presents a "low at both ends and high in the middle" pattern, which is significantly affected by wall width and roadway size: when the wall width is ≥1.4 m, stress concentration is effectively alleviated; narrow roadways enhance the collaborative bearing effect between coal-rock masses and the wall. The SSA-ELM model achieves high prediction performance, with correlation coefficients (R) of 0.995 and 0.993 for the training and test sets, respectively. Compared with traditional machine learning algorithms (BP neural network, SVM, random forest), the model reduces the average prediction error by approximately 31%, with a mean absolute percentage error (MAPE) of 1.0251%.
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