發(fā)布時(shí)間：2018-06-09 04:04

  本文選題：變形監(jiān)測(cè) + 小波變換閾值去噪　； 參考：《長(zhǎng)安大學(xué)》2014年碩士論文

【摘要】：近些年來(lái)，得益于城鎮(zhèn)化和現(xiàn)代化步伐不斷提速，基礎(chǔ)設(shè)施建設(shè)得到了大幅度的發(fā)展,在我們的生活之中越來(lái)越多的大型建筑物和構(gòu)筑物如雨后春筍般拔地而起，毋庸置疑這些大型建筑物給我們的生活帶來(lái)很多便利，但是同時(shí)也隱藏著許多安全問(wèn)題。在建筑物施工建設(shè)和運(yùn)營(yíng)管理過(guò)程中，為了避免可能出現(xiàn)的突發(fā)狀況,需要定期的監(jiān)測(cè)建筑物的沉降,以獲得沉降變形數(shù)據(jù)。采取一些方法對(duì)所獲得的數(shù)據(jù)進(jìn)行處理和分析,盡量準(zhǔn)確地預(yù)測(cè)出建筑物變形的大致趨勢(shì),這樣我們才能有針對(duì)性地采取一些防范措施，避免突發(fā)狀況或?yàn)?zāi)難的發(fā)生。 根據(jù)觀測(cè)和分析可以知道,建筑物沉降觀測(cè)數(shù)據(jù)是一系列短序列的離散數(shù)據(jù),它具有包含噪聲并且波動(dòng)性較大的特點(diǎn)。本文在進(jìn)行研究工作時(shí)分析了目前常用的一些變形監(jiān)測(cè)數(shù)據(jù)處理以及變形預(yù)測(cè)的理論和方法,并且在前人研究的基礎(chǔ)上,結(jié)合自己對(duì)變形監(jiān)測(cè)研究的一些思考,提出了基于小波變換閾值去噪的灰色馬爾可夫鏈預(yù)測(cè)模型,并采用監(jiān)測(cè)實(shí)例對(duì)其加以應(yīng)用和驗(yàn)證。論文以西安宏信國(guó)際花園4號(hào)樓沉降變形監(jiān)測(cè)數(shù)據(jù)為作為例子,根據(jù)該項(xiàng)目沉降監(jiān)測(cè)點(diǎn)位布設(shè)情況，，應(yīng)用提出的方法對(duì)其中若干個(gè)代表性較強(qiáng)的監(jiān)測(cè)點(diǎn)進(jìn)行了分析研究。在數(shù)據(jù)處理方面,首先,用MATLAB編寫(xiě)小波去噪程序用以小波分解及小波重構(gòu),得到去噪后的有用數(shù)據(jù)；其次,利用去經(jīng)過(guò)小波噪后的擬合數(shù)據(jù)建立自適應(yīng)加權(quán)灰色預(yù)測(cè)模型并預(yù)測(cè)其沉降值,以預(yù)測(cè)值與觀測(cè)值的相對(duì)誤差為劃分標(biāo)準(zhǔn)對(duì)預(yù)測(cè)值劃分狀態(tài)區(qū)間,再利用馬爾可夫鏈預(yù)測(cè)方法判斷出未來(lái)某一時(shí)刻沉降量的狀態(tài)可能處于的區(qū)間,得出狀態(tài)概率矩陣，從而利用馬氏鏈理論求得建筑物的更優(yōu)化的沉降預(yù)測(cè)值；最后,將小波變換灰色馬爾科夫鏈、單純的灰色模型以及小波灰色模型三種預(yù)測(cè)方法所得到的預(yù)測(cè)結(jié)果進(jìn)行對(duì)比分析，得出有益結(jié)論。根據(jù)對(duì)比結(jié)果可知,小波變換灰色馬爾可夫預(yù)測(cè)結(jié)果要優(yōu)于單純的灰色模型預(yù)測(cè)和小波變換灰色預(yù)測(cè)結(jié)果,也就是說(shuō)它可以進(jìn)一步提高預(yù)測(cè)精度。在處理波動(dòng)性較大的數(shù)據(jù)時(shí),利用基于小波變換的灰色馬爾可夫鏈模型能得出更優(yōu)化的預(yù)測(cè)結(jié)果,因此，這個(gè)模型也為隨機(jī)波動(dòng)性較大的數(shù)據(jù)序列提供了一種新的數(shù)據(jù)處理與預(yù)測(cè)方法。
[Abstract]:In recent years, thanks to the increasing pace of urbanization and modernization, infrastructure construction has been greatly developed, and more large buildings and structures have sprung up in our lives. There is no doubt that these large buildings bring a lot of convenience to our life, but there are also many safety problems. In the process of building construction and operation management, in order to avoid the unexpected situation, it is necessary to monitor the settlement of buildings regularly in order to obtain settlement deformation data. We should take some methods to process and analyze the obtained data, and try our best to predict the approximate trend of the deformation of the building, so that we can take some preventive measures in a targeted way. According to the observation and analysis, we can know that the observation data of building settlement is a series of discrete data of short series, which has the characteristics of noise and volatility. In this paper, some commonly used theories and methods of deformation monitoring data processing and deformation prediction are analyzed, and based on previous studies, some thoughts on deformation monitoring research are combined. A grey Markov chain prediction model based on wavelet transform threshold denoising is proposed, and it is applied and verified by a monitoring example. Based on the monitoring data of settlement deformation in the 4th Building of Hongxin International Garden in Xi'an as an example, according to the settlement monitoring site layout of the project, several representative monitoring points are analyzed and studied by using the proposed method. In the aspect of data processing, firstly, the wavelet de-noising program is written with MATLAB to decompose and reconstruct the wavelet to get the useful data after denoising. The adaptive weighted grey prediction model is established and its settlement value is predicted by using the fitting data after wavelet denoising. The state interval of the predicted value is divided according to the relative error between the predicted value and the observed value. Then the Markov chain prediction method is used to determine the possible interval of the state of settlement at a certain time in the future, and the state probability matrix is obtained, thus the more optimal settlement prediction value of the building can be obtained by using Markov chain theory. Finally, The prediction results obtained by wavelet transform grey Markov chain, simple grey model and wavelet grey model are compared and analyzed, and some useful conclusions are drawn. According to the comparative results, the grey Markov prediction results of wavelet transform are superior to those of pure grey model prediction and wavelet transform grey prediction results, that is to say, it can further improve the prediction accuracy. When dealing with volatile data, the grey Markov chain model based on wavelet transform can be used to obtain more optimized prediction results. This model also provides a new method of data processing and prediction for random data series with high volatility.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TU196

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