CAJ | 학술논문

基于人工神经网络基本原理，对煤矿立井基岩含水层涌水危险性进行了预测。利用归纳分析得出的煤矿立井基岩含水层涌水主控因素，构建了煤矿立井基岩段含水层涌水危险ANN（Artificial Neural Network）型脆弱性指数预测模型，并将其运用于梵王寺煤矿副井基岩含水层涌水危险性预测工程实践。结果表明，脆弱性指数预测模型能够较好地评价立井基岩含水层涌水影响因素，预测结果对基岩段凿井施工具有重要参考价值。
기우인공신경망락기본원리，대매광립정기암함수층용수위험성진행료예측。이용귀납분석득출적매광립정기암함수층용수주공인소，구건료매광립정기암단함수층용수위험ANN（Artificial Neural Network）형취약성지수예측모형，병장기운용우범왕사매광부정기암함수층용수위험성예측공정실천。결과표명，취약성지수예측모형능구교호지평개립정기암함수층용수영향인소，예측결과대기암단착정시공구유중요삼고개치。
Based on a basic principle of the artificial neural network ,a prediction was conducted on the water inflow danger from the aquifer in the base rock of the mine shaft. The main control factors of the water inflow from the aquifer in the base rock of the mine shaft obtained with an inductive analysis were applied to establish an ANN mode vulnerability index prediction model of the water inflow dan‐ger from the aquifer in the base rock section of the mine shaft. The model was applied to the prediction engineering practices of the water inflow danger in the base rock of an auxiliary shaft in Fanwangsi Mine. The results showed that the vulnerability index prediction model could well evaluate the water inflow influence factors of the aquifer in the base rock of the mine shaft and predicted results would have important reference value to the mine shaft sinking operations at the base rock section.