刘艳章,邹晓甜,潘世华,陈小强,张群,张丙涛,柯丽华

• 1:武汉科技大学资源与环境工程学院

摘要(Abstract)：

为研究爆破振动对金山店铁矿地表构筑物和井下巷道的影响,引入广义回归神经网络(GRNN)的方法,分别以地表、井下部分振动监测数据为学习样本对GRNN进行训练,构建地表、井下爆破振动峰值速度的GRNN预测模型,以剩余振动监测数据为检测样本对地表和井下GRNN预测模型进行检验,并将GRNN模型的预测结果与BPNN、基函数回归法和经验公式法作对比。同时,将地表、井下GRNN模型的预测结果与以地表和井下综合训练数据为学习样本构建的综合GRNN预测模型进行对比。研究结果表明:对于地表监测点,四种方法的预测误差率依次为12.1%、18.9%、30.3%、43.7%;对于井下监测点,四种方法的预测误差率依次为14.0%、16.2%、19.9%、23.0%。GRNN的预测精度最高,其为爆破振动峰值速度的预测提供了一种新方法,且采用GRNN对地表、井下质点爆破振动峰值速度分别进行预测更加合理。

关键词(KeyWords)： 金山店铁矿;GRNN;爆破振动峰值速度;爆破振动预测

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金面上项目(编号:51074115);国家自然科学基金青年项目(51204127);; 湖北省自然科学基金重点项目(2015CFA142)

作者(Author): 刘艳章,邹晓甜,潘世华,陈小强,张群,张丙涛,柯丽华

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