發(fā)布時(shí)間：2018-01-12 04:22

  本文關(guān)鍵詞：仙女巖隧道下穿油氣管道段爆破振動(dòng)效應(yīng)預(yù)測(cè)研究　出處：《西南交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： EMD分解 BP神經(jīng)網(wǎng)絡(luò) 支持向量機(jī)

【摘要】：本文以西成客專仙女巖隧道出口段掘進(jìn)爆破為工程背景。由于隧道下穿油氣管道,并且隧道走向與天然氣管道走向相交,垂直距離約55m。隧道采用鉆爆法施工,必然會(huì)對(duì)周圍巖層及油氣管道造成一定影響。我們需要控制對(duì)掘進(jìn)爆破過(guò)程中所產(chǎn)生的影響,達(dá)到確保油氣管道安全的目的。本文對(duì)現(xiàn)場(chǎng)監(jiān)測(cè)數(shù)據(jù)進(jìn)行地表爆破振動(dòng)特性的分析得到在掘進(jìn)爆破過(guò)程不同類型的炮孔產(chǎn)生了不同程度的波的疊加。通過(guò)頻譜分析得到隨著測(cè)點(diǎn)與掌子面的距離不斷增大,振動(dòng)速度主頻率也相應(yīng)提高,單段裝藥量的增加,相應(yīng)的峰值振動(dòng)速度的主頻率反而降低的規(guī)律。并且EMD分解能選擇性的濾掉不需要的波,而通過(guò)重構(gòu)波的幅值包絡(luò)圖能分析出每段雷管實(shí)際起爆時(shí)間及之間的延遲。通過(guò)實(shí)測(cè)數(shù)據(jù)的回歸擬合分析,得出該工程并不符合薩道夫斯基的經(jīng)驗(yàn)公式,需要采取其他方法進(jìn)行爆破振動(dòng)峰值預(yù)測(cè)。本文選擇BP神經(jīng)網(wǎng)絡(luò)、GA-BP、PSO-SVM、 GA-SVM四種預(yù)測(cè)方法,通過(guò)實(shí)測(cè)數(shù)據(jù)進(jìn)行訓(xùn)練,并得到每種方法的預(yù)測(cè)結(jié)果。通過(guò)預(yù)測(cè)結(jié)果對(duì)比,誤差相對(duì)百分比依次為GA-BP、PSO-SVM、GA-SVM、BP神經(jīng)網(wǎng)絡(luò)。其中GA-BP、PSO-SVM預(yù)測(cè)誤差百分比均很小,然而GA-BP的拓?fù)浣Y(jié)構(gòu)依然是BP神經(jīng)網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu),具有一定的不穩(wěn)定性,而支持向量機(jī)克服了傳統(tǒng)的神經(jīng)網(wǎng)絡(luò)所固有且無(wú)法避免的缺陷,并且大多數(shù)工程中都是小樣本預(yù)測(cè)。綜合而言采用PSO-SVM方法進(jìn)行預(yù)測(cè),效果最佳。
[Abstract]:The project background is the driving blasting of the exit section of the Xianxian tunnel in the west of this paper. Because of the passage of oil and gas pipeline under the tunnel and the intersection of the tunnel direction and the natural gas pipeline direction. The vertical distance is about 55m.The tunnel is constructed by drilling and blasting method, which will inevitably affect the surrounding rock formations and oil and gas pipelines. We need to control the impact on the excavation blasting process. In order to ensure the safety of oil and gas pipeline, this paper analyzes the vibration characteristics of surface blasting with the monitoring data on the spot. It is concluded that different types of blasting holes have different degrees of wave superposition in the process of tunneling blasting. The results show that the distance between the measuring point and the palm surface is increasing. The main frequency of the vibration velocity also increases correspondingly, and the main frequency of the corresponding peak vibration velocity decreases with the increase of the charge quantity in the single section. Moreover, the EMD decomposition can selectively filter out the unwanted wave. Through the reconstruction of the amplitude envelope diagram, the actual detonator initiation time and the delay between the detonators can be analyzed. Through the regression fitting analysis of the measured data, it is concluded that the project does not accord with the empirical formula of Sadolski. It is necessary to adopt other methods to predict the peak value of blasting vibration. In this paper, the BP neural network and four prediction methods of PSO-SVM and GA-SVM are selected and trained by the measured data. The relative percentage of the error is GA-BPU PSO-SVMN GA-SVMBP neural network, in which GA-BP. The percentage of PSO-SVM prediction error is very small, but the topological structure of GA-BP is still the topological structure of BP neural network, which has some instability. Support vector machine (SVM) overcomes the inherent and unavoidable defects of traditional neural networks, and most of the projects are small sample prediction. In summary, PSO-SVM method is the best method for prediction.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U455.41

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本文編號(hào)：1412669