本文關(guān)鍵詞： 圍巖位移 時間序列預(yù)測 支持向量機(jī) 小波核函數(shù)　出處：《重慶交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：新奧法是隧道工程建設(shè)中廣泛使用的方法。監(jiān)控量測是新奧法中的重要組成部分,量測的數(shù)據(jù)能夠體現(xiàn)隧道圍巖的變形狀態(tài),依托監(jiān)測數(shù)據(jù)對隧道圍巖的變形進(jìn)行預(yù)測能夠很好的為隧道的設(shè)計和施工提供參考價值,現(xiàn)今的位移反演分析能夠?qū)λ淼肋M(jìn)行一定范圍內(nèi)的預(yù)測,但反演分析主要依托在沉降穩(wěn)定值的反推與鄰近界面的材料特性相似性上,有一定的滯后性不能滿足工程建設(shè)中時間投資上的要求。利用量測數(shù)據(jù)的自規(guī)律性對位移變形進(jìn)行預(yù)測的研究還較少。針對位移變化的時間序列預(yù)測上,本文依托統(tǒng)計學(xué)習(xí)理論中的支持向量機(jī)開展研究,主要工作有:①介紹了支持向量機(jī)的理論基礎(chǔ)與具體的推導(dǎo)過程,利用陽宗隧道的位移監(jiān)測數(shù)據(jù)實例分析了不同超參數(shù)組合下的支持向量機(jī)性能,提出了參數(shù)選擇的方法與依據(jù)。對比了多項式回歸預(yù)測與遞歸最小二乘法預(yù)測的差異,并用MATLAB編程依托推導(dǎo)的原理進(jìn)行了計算分析,結(jié)果表明支持向量機(jī)具有較好的穩(wěn)定性。②以京珠高速公路溫泉隧道位移非線性時間序列預(yù)測為例子,對比了不同特征表現(xiàn)形式下的RBF核支持向量機(jī)預(yù)測性能。分析結(jié)果表明,采用累加和的位移表現(xiàn)形式能有效提升RBF核函數(shù)下的支持向量機(jī)預(yù)測性能,對比了傳統(tǒng)歸一化方法與對數(shù)空間映射的差異性,分析結(jié)果表明對于同一個映射空間下的子集選擇RBF核函數(shù)不敏感。③針對RBF核函數(shù)優(yōu)良的特性,通過小波理論與核函數(shù)的特性構(gòu)造出了Morlet小波核函數(shù),并經(jīng)過溪洛渡電站隧道的監(jiān)測數(shù)據(jù)的分析驗證,Morlet小波核函數(shù)在監(jiān)測頻率不同的位移變形預(yù)測分析上,有較好的預(yù)測性能。
[Abstract]:New Olympic method is widely used in tunnel construction. Monitoring and measurement is an important part of the method. The measured data can reflect the deformation state of tunnel surrounding rock. Relying on monitoring data to predict the deformation of tunnel surrounding rock can provide a good reference value for the design and construction of the tunnel. Now the displacement inversion analysis can predict the tunnel in a certain range. But the inversion analysis is mainly based on the similarity of the material properties between the stability value of settlement and the material characteristics of the adjacent interface. Some lag can not meet the requirements of time investment in engineering construction. There is little research on the prediction of displacement deformation by using the self-regularity of measurement data. Based on the support vector machine (SVM) in statistical learning theory, the main work of this paper is to introduce the theoretical basis and the specific derivation process of support vector machine (SVM). Based on the displacement monitoring data of Yangzong tunnel, the performance of support vector machine (SVM) under different super-parameter combinations is analyzed. The method and basis of parameter selection are put forward, the difference between polynomial regression prediction and recursive least square prediction is compared, and the calculation and analysis are carried out based on the principle of MATLAB programming. The results show that the support vector machine has good stability. 2. The prediction of nonlinear time series of displacement in hot spring tunnel of Jingzhou-Zhuhai Expressway is taken as an example. The prediction performance of RBF kernel support vector machine with different feature forms is compared. The analysis results show that the prediction performance of support vector machine under RBF kernel function can be improved effectively by using the displacement representation of cumulative sum. The difference between the traditional normalization method and the logarithmic space mapping is compared. The results show that the RBF kernel function is insensitive to the RBF kernel function for the subset of the same mapping space. The Morlet wavelet kernel function is constructed by wavelet theory and kernel function, and verified by the monitoring data of Xiluodu Hydropower Station. Morlet wavelet kernel function has good prediction performance in monitoring displacement and deformation prediction with different frequencies.
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U456.31

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本文編號：1489623