本文選題：地鐵隧道變形監(jiān)測(cè)　切入點(diǎn)：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(guó)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(yùn)行中由于受多種因素影響會(huì)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(shí)有效的預(yù)報(bào)模型具有重要的意義,但各個(gè)模型都有局限性,單一的模型往往預(yù)測(cè)精度較低,因此需要將現(xiàn)有模型有針對(duì)性的組合和優(yōu)化。地鐵隧道變形數(shù)據(jù)具有動(dòng)態(tài)、平穩(wěn)、含噪聲的特點(diǎn),時(shí)間序列分析在處理預(yù)測(cè)動(dòng)態(tài)平穩(wěn)信號(hào)時(shí)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(hào)中的噪聲部分,從而提高預(yù)測(cè)精度。本文提出小波和時(shí)間序列分析組合模型,對(duì)地鐵隧道變形進(jìn)行預(yù)測(cè)。論文主要的研究?jī)?nèi)容如下:(1)地鐵隧道變形預(yù)測(cè)方法和變形情況分析研究地鐵變形預(yù)測(cè)方法,從理論基礎(chǔ)、分析方法、數(shù)據(jù)量要求和研究重點(diǎn)等方面對(duì)常用方法特點(diǎn)進(jìn)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(cè)量技術(shù)要求等內(nèi)容;以南京地鐵十號(hào)線隧道結(jié)構(gòu)沉降數(shù)據(jù)為例,分析地鐵隧道單點(diǎn)變形數(shù)據(jù)特征以及全線監(jiān)測(cè)點(diǎn)、車站主體結(jié)構(gòu)及各個(gè)區(qū)間的變形情況。(2)小波分析和時(shí)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(guò)改變小波函數(shù)和閾值估計(jì)方法進(jìn)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(jì)方法;研究時(shí)間序列分析的分類、特點(diǎn)以及AR、MA、ARMA模型基本原理,重點(diǎn)研究模型識(shí)別、定階和參數(shù)估計(jì)方法,利用地鐵隧道變形數(shù)據(jù)進(jìn)行單一時(shí)間序列分析建模和預(yù)測(cè)。(3)組合模型的構(gòu)建和實(shí)例驗(yàn)證結(jié)合小波和時(shí)間序列分析模型的特點(diǎn),通過(guò)兩種不同組合方式分別進(jìn)行擬合預(yù)測(cè)并與單一模型擬合預(yù)測(cè)結(jié)果進(jìn)行比較,驗(yàn)證組合模型由于去除了原始信號(hào)中的噪聲,信號(hào)變的更加平滑,使時(shí)間序列分析充分發(fā)揮它在處理平穩(wěn)信號(hào)時(shí)的優(yōu)勢(shì),取得更好的擬合效果;通過(guò)評(píng)價(jià)指標(biāo)驗(yàn)證對(duì)去噪后的分量進(jìn)行時(shí)序預(yù)測(cè)再重構(gòu)的組合模型,擬合準(zhǔn)確度和預(yù)測(cè)精度更高;最后利用效果更好的組合方式對(duì)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(jìn)行預(yù)測(cè),研究變形趨勢(shì)并分析變形原因,有利于及時(shí)發(fā)現(xiàn)問(wèn)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U456.3

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