摘要
简要介绍了用于监测岩体稳定性的声发射源定位系统ＳＤＬ－１和便携式声发射智能监测仪ＤＹＦ－１，这些仪器能获取一个声发射事件所包含的尽量多的信息，基于这些信息开发了一种有效可靠的预测冒顶技术。该技术利用多个声发射参数（ＡＥ事件率、ＡＥ能量和ｍ值）评价声发射活动，在这些参数的监测数据基础上应用灰色系统理论预测将来的声发射，预测值通过训练好的冒顶模式识别，由人工神经网络模型输出对应的冒顶模式（较大规模的顶板塌落、小规模掉块和稳定）。实例研究结果表明。
A mine wide Acoustic Emission( AE ) Source Location System(SDL 1) and a Portable Intelligent AE Monitoring Device(DVF 1) were described. These instruments can acquire AE data as much as those associated with an AE event. An effective and reliable technique to predict the occurrence of roof fall hazards has been developed, based on the AE data measured. It has utilized several AE indicators( AE rate, AE energy and m value) to evaluate the AE activity and applied grey system theory to construct grey prediction models for forecasts on AE. The predicted values of AE indicators could then be input to the trained artificial neural network 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 forecast on the occurrence of roof fall modes has a good agreement with the practice.
出处
《中国有色金属学报》
EI
CAS
CSCD
1996年第2期7-12,共6页
The Chinese Journal of Nonferrous Metals
关键词
冒顶
声发射
预测
预报
采场
roof fall mode acoustic emission grey theory neural network prediction
