基于人工神经网络的厚松散层立井井壁破坏预测研究

煤矿立井井壁破坏是由多种地质与工程因素综合作用的结果,在深入分析井壁破坏的机理与影响因素的基础上,收集整理了兖州、临涣、宿县和大屯矿区14个立井数据建立样本集,引入非线性的人工神经网络技术,建立了结构为7-4-1的井壁破坏预测BP神经网络模型,并对兴隆庄矿4个立井井壁破坏时间进行了预测。

Prediction and Research of Vertical Shaft Wall Failure in Loose Ground Based on Artificial Neural Network

The vertical shaft wall failure in a coal mine resulted from the integral action and influence of various geologic and engineering factors. Based on the deep analysis of the failure mechanism and influence factors of the shaft wall, the authors collected and sorted out the data setup examples of 14 vertical shafts in Yanzhou, Linhuan, Suxian and Datun Coal Mining Areas, introduced non-linear artificial NN technique, set up BP NN model for predicting shaft wall failure of 7-4-1 structure and made prediction of the wall failure time of four vertical shafts in Xinglongzhong Coal Mine.