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》2017年碩士論文　論文類型：學位論文

【摘要】：目前我國各大城市均在建設高效的地鐵隧道網(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重后果,所以建立及時有效的預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處理工具,有效消去原始信號中的噪聲部分,從而提高預測精度。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測。論文主要的研究內(nèi)容如下:(1)地鐵隧道變形預測方法和變形情況分析研究地鐵變形預測方法,從理論基礎、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,基準網(wǎng)布設方法、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測并與單一模型擬合預測結(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;通過評價指標驗證對去噪后的分量進行時序預測再重構(gòu)的組合模型,擬合準確度和預測精度更高;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.
【學位授予單位】：南京師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U456.3

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