2026-10-14T12:14:03Z https://www.scidb.cn/oai

10.57760/sciencedb.36935 2026-02-05T10:54:01Z

2026-02-05 Data of SSA-ELM Prediction Model for Roadside Support Stress in No-Pillar Mining doi:10.57760/sciencedb.36935 en Aiming at the problem that the complex stress evolution of roadside walls in no-pillar coal mining makes it difficult to accurately judge the wall state, a mining-induced stress response database was constructed through FLAC3D numerical simulation, and an intelligent stress prediction model based on the Sparrow Search Algorithm optimized Extreme Learning Machine (SSA-ELM) was established. The parameters used for model input in the database include: mining distance, wall width, burial depth, roadway width, position, wall height, strike length, and dip length. Machine learning; Gob-side entry retaining; Roadside wall stress; SSA-ELM model; Intelligent prediction Furong Tang Ye Tian Yongcheng Liu Songzhao Li Ke Wang PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank