本文選題：金山店鐵礦 + GRNN��； 參考：《爆破》2017年02期

【摘要】：為研究爆破振動對金山店鐵礦地表構(gòu)筑物和井下巷道的影響,引入廣義回歸神經(jīng)網(wǎng)絡(luò)(GRNN)的方法,分別以地表、井下部分振動監(jiān)測數(shù)據(jù)為學(xué)習(xí)樣本對GRNN進(jìn)行訓(xùn)練,構(gòu)建地表、井下爆破振動峰值速度的GRNN預(yù)測模型,以剩余振動監(jiān)測數(shù)據(jù)為檢測樣本對地表和井下GRNN預(yù)測模型進(jìn)行檢驗,并將GRNN模型的預(yù)測結(jié)果與BPNN、基函數(shù)回歸法和經(jīng)驗公式法作對比。同時,將地表、井下GRNN模型的預(yù)測結(jié)果與以地表和井下綜合訓(xùn)練數(shù)據(jù)為學(xué)習(xí)樣本構(gòu)建的綜合GRNN預(yù)測模型進(jìn)行對比。研究結(jié)果表明:對于地表監(jiān)測點,四種方法的預(yù)測誤差率依次為12.1%、18.9%、30.3%、43.7%;對于井下監(jiān)測點,四種方法的預(yù)測誤差率依次為14.0%、16.2%、19.9%、23.0%。GRNN的預(yù)測精度最高,其為爆破振動峰值速度的預(yù)測提供了一種新方法,且采用GRNN對地表、井下質(zhì)點爆破振動峰值速度分別進(jìn)行預(yù)測更加合理。
[Abstract]:In order to study the influence of blasting vibration on the surface structures and underground tunnels of jinjindian iron mine, the generalized regression neural network (GRNN) was introduced to train the GRNN in the ground surface and the underground part of the vibration monitoring data. The GRNN prediction model of the ground surface and the blasting vibration peak velocity of the underground mine was constructed, and the residual vibration monitoring data were taken as the data. The test samples are tested on the ground surface and the downhole GRNN prediction model, and the prediction results of the GRNN model are compared with the BPNN, the base function regression method and the empirical formula method. At the same time, the prediction results of the ground surface and the downhole GRNN model are compared with the comprehensive GRNN prediction model constructed with the ground surface and the underground comprehensive training data for the learning samples. The results show that the prediction error rates of the four methods are 12.1%, 18.9%, 30.3% and 43.7% for the surface monitoring points. For the underground monitoring points, the prediction error rates of the four methods are 14%, 16.2% and 19.9%, and the 23.0%.GRNN has the highest prediction accuracy. It provides a new method for the prediction of the peak velocity of blasting vibration, and uses GRNN to the surface and underground. It is more reasonable to predict the peak velocity of particle blasting vibration respectively.

【作者單位】： 武漢科技大學(xué)資源與環(huán)境工程學(xué)院;
【基金】：國家自然科學(xué)基金面上項目(編號:51074115);國家自然科學(xué)基金青年項目(51204127) 湖北省自然科學(xué)基金重點項目(2015CFA142)
【分類號】：TD235.1

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本文編號：1803771