(長沙礦山研究院，長沙410012)
摘 要: 簡要介紹了用于監(jiān)測巖體穩(wěn)定性的聲發(fā)射源定位系統(tǒng)SDL- 1和便攜式聲發(fā)射智能監(jiān)測儀DYF- 1，這些儀器能獲取一個聲發(fā)射事件所包含的盡量多的信息，基于這些信息開發(fā)了一種有效可靠的預(yù)測冒頂技術(shù)。該技術(shù)利用多個聲發(fā)射參數(shù)(AE事件率、AE能量和‑m值)評價(jià)聲發(fā)射活動，在這些參數(shù)的監(jiān)測數(shù)據(jù)基礎(chǔ)上應(yīng)用灰色系統(tǒng)理論預(yù)測將來的聲發(fā)射，預(yù)測值通過訓(xùn)練好的冒頂模式識別，由于人工神經(jīng)網(wǎng)絡(luò)模型輸出對應(yīng)的冒頂模式(較大規(guī)模的頂板塌落、小規(guī)模掉塊和穩(wěn)定)。實(shí)例研究結(jié)果表明，該方法的預(yù)測結(jié)果與實(shí)際情形具有很好的一致性。
關(guān)鍵字: 冒頂模式 聲發(fā)射 灰色理論 神經(jīng)網(wǎng)絡(luò) 預(yù)測預(yù)報(bào)
（Changsha Institute of Mining Research, Changsha 410012）
Abstract:A mine-wide A coustic Emission(AE) Source Location System(SDL-1)and a Portable Intelligent AE M on it oring Device(DVF-1)were described . These instruments can acquire AE dat as much as those associated with an AE event . An effective and reliable technique to predict the occurrence of roof fall hazards has been devel-oped, based on the AE data measured. It has utilized several AE indicators(AE rate, AE energy and m value )to e-valuta the AE activity and applied grey system theory to the trained artifivial neural net work for adaptive roof fall modes recognition to automatically identify the roof fall modes(collapse, small blocks fall and stable).The case study showed that the result of forecase on the occurrence of roof fall modes has a good agreement with the practice.
Key words: roof fall mode acoustic emission grey theory neural network prediction
