本文選題：地鐵隧道變形監(jiān)測　切入點：小波去噪　出處：《南京師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：目前我國各大城市均在建設(shè)高效的地鐵隧道網(wǎng),地鐵隧道在施工和運行中由于受多種因素影響會產(chǎn)生變形,變形如果超出安全范圍將引起嚴(yán)重后果,所以建立及時有效的預(yù)報模型具有重要的意義,但各個模型都有局限性,單一的模型往往預(yù)測精度較低,因此需要將現(xiàn)有模型有針對性的組合和優(yōu)化。地鐵隧道變形數(shù)據(jù)具有動態(tài)、平穩(wěn)、含噪聲的特點,時間序列分析在處理預(yù)測動態(tài)平穩(wěn)信號時有很好的效果,而小波分析能夠作為預(yù)處理工具,有效消去原始信號中的噪聲部分,從而提高預(yù)測精度。本文提出小波和時間序列分析組合模型,對地鐵隧道變形進行預(yù)測。論文主要的研究內(nèi)容如下:(1)地鐵隧道變形預(yù)測方法和變形情況分析研究地鐵變形預(yù)測方法,從理論基礎(chǔ)、分析方法、數(shù)據(jù)量要求和研究重點等方面對常用方法特點進行分析與比較;研究地鐵隧道結(jié)構(gòu)沉降的影響因素,基準(zhǔn)網(wǎng)布設(shè)方法、測量技術(shù)要求等內(nèi)容;以南京地鐵十號線隧道結(jié)構(gòu)沉降數(shù)據(jù)為例,分析地鐵隧道單點變形數(shù)據(jù)特征以及全線監(jiān)測點、車站主體結(jié)構(gòu)及各個區(qū)間的變形情況。(2)小波分析和時間序列分析模型研究研究小波變換和閾值去噪的基本理論,通過改變小波函數(shù)和閾值估計方法進行去噪效果比較,選取適合本文數(shù)據(jù)的小波函數(shù)以及閾值估計方法;研究時間序列分析的分類、特點以及AR、MA、ARMA模型基本原理,重點研究模型識別、定階和參數(shù)估計方法,利用地鐵隧道變形數(shù)據(jù)進行單一時間序列分析建模和預(yù)測。(3)組合模型的構(gòu)建和實例驗證結(jié)合小波和時間序列分析模型的特點,通過兩種不同組合方式分別進行擬合預(yù)測并與單一模型擬合預(yù)測結(jié)果進行比較,驗證組合模型由于去除了原始信號中的噪聲,信號變的更加平滑,使時間序列分析充分發(fā)揮它在處理平穩(wěn)信號時的優(yōu)勢,取得更好的擬合效果;通過評價指標(biāo)驗證對去噪后的分量進行時序預(yù)測再重構(gòu)的組合模型,擬合準(zhǔn)確度和預(yù)測精度更高;最后利用效果更好的組合方式對變形突出的中勝站、龍華路站以及中勝—元通區(qū)間的沉降量進行預(yù)測,研究變形趨勢并分析變形原因,有利于及時發(fā)現(xiàn)問題并采取相應(yīng)措施。
[Abstract]:At present, all the major cities in our country are building an efficient subway tunnel network. The subway tunnel will be deformed due to the influence of many factors in its construction and operation. If the deformation exceeds the safe range, it will cause serious consequences. Therefore, it is of great significance to establish a timely and effective forecasting model, but each model has its limitations, and a single model often has low prediction accuracy. Therefore, it is necessary to combine and optimize the existing models. The deformation data of subway tunnel have the characteristics of dynamic, steady and noisy. The time series analysis has a good effect in the prediction of dynamic stationary signals. Wavelet analysis can be used as a preprocessing tool to effectively eliminate the noise part of the original signal, thus improving the prediction accuracy. In this paper, a combined model of wavelet and time series analysis is proposed. The main contents of this paper are as follows: 1) the method of subway tunnel deformation prediction and the analysis of subway deformation. This paper analyzes and compares the characteristics of common methods from the aspects of data requirement and research emphasis, studies the factors affecting the settlement of subway tunnel structure, the method of setting up the benchmark network, and the technical requirements of measurement, and so on. Taking the settlement data of the tunnel structure of Nanjing Metro Line 10 as an example, the characteristics of the single point deformation data of the subway tunnel and the monitoring points of the whole line are analyzed. Wavelet analysis and time series analysis model study the basic theory of wavelet transform and threshold denoising, and compare the denoising effect by changing wavelet function and threshold estimation method. The wavelet function and threshold estimation method suitable for the data of this paper are selected, the classification and characteristics of time series analysis and the basic principle of ARMA-ARMA model are studied, and the methods of model identification, order determination and parameter estimation are emphatically studied. Using the deformation data of subway tunnel to model and predict the single time series analysis. The construction of the combined model and the verification of the example show the characteristics of the wavelet and time series analysis model. The results of fitting and forecasting by two different combinations are compared with the results of single model. It is verified that the combined model is smoother because of removing the noise from the original signal. Make the time series analysis give full play to its advantages in processing stationary signals, obtain better fitting effect, verify the combination model of the de-noised components for time series prediction and re-reconstruct through the evaluation index, the fitting accuracy and prediction accuracy are higher. Finally, the settlement of Zhongsheng Station, Longhua Road Station and Zhongsheng-Yuantong Section with outstanding deformation are forecasted by better combination method, and the trend of deformation is studied and the cause of deformation is analyzed, which is helpful to find the problem and take corresponding measures in time.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U456.3

【參考文獻】

相關(guān)期刊論文 前10條

1 張善廷;杜超;李勇;;灰色-時序組合模型在地表變形預(yù)測中的應(yīng)用[J];測繪與空間地理信息;2017年01期

2 馬玉梅;;多元回歸分析方法在高層建筑沉降監(jiān)測數(shù)據(jù)處理中的應(yīng)用[J];測繪與空間地理信息;2016年12期

3 李進;黃張裕;歐陽經(jīng)富;王存有;;灰色神經(jīng)網(wǎng)絡(luò)組合模型在變形監(jiān)測數(shù)據(jù)分析中的應(yīng)用[J];勘察科學(xué)技術(shù);2016年05期

4 黃永紅;;灰色-BP神經(jīng)網(wǎng)絡(luò)在深基坑變形預(yù)測中的應(yīng)用研究[J];四川理工學(xué)院學(xué)報(自然科學(xué)版);2016年05期

5 孫清娟;師軍良;;回歸分析在大橋沉降監(jiān)測預(yù)測中的應(yīng)用[J];測繪通報;2016年07期

6 寧昕揚;劉曉青;;基于MIV-改進RBF神經(jīng)網(wǎng)絡(luò)的大壩變形監(jiān)測模型[J];三峽大學(xué)學(xué)報(自然科學(xué)版);2016年03期

7 蔣晨;張書畢;文小勇;;基于中位數(shù)回歸分析的礦區(qū)變形監(jiān)測數(shù)據(jù)處理[J];金屬礦山;2016年05期

8 楊昌民;崔兵;張忠強;;自適應(yīng)卡爾曼濾波在建筑物變形監(jiān)測中的應(yīng)用[J];北京測繪;2016年02期

9 齊秀峰;;基于量子神經(jīng)網(wǎng)絡(luò)擬合法的礦區(qū)地表變形監(jiān)測[J];金屬礦山;2016年04期

10 申意保;;變形監(jiān)測回歸分析模型的優(yōu)化改進[J];低碳世界;2016年11期

相關(guān)博士學(xué)位論文 前1條

1 楊廣武;地下工程穿越既有地鐵線路變形控制標(biāo)準(zhǔn)和技術(shù)研究[D];北京交通大學(xué);2010年

相關(guān)碩士學(xué)位論文 前1條

1 王洋;基于時間序列分析的IP語音收入預(yù)測[D];吉林大學(xué);2004年

，

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