發(fā)布時(shí)間：2018-04-14 22:36

  本文選題：變形監(jiān)測 + 回歸分析��； 參考：《中南大學(xué)》2014年碩士論文

【摘要】：通過對(duì)變形監(jiān)測數(shù)據(jù)進(jìn)行分析,發(fā)現(xiàn)變形規(guī)律并建立模型對(duì)變形進(jìn)行預(yù)報(bào)預(yù)測,是變形監(jiān)測的一項(xiàng)主要工作。其中,回歸分析作為一種傳統(tǒng)的變形分析方法,具有建模簡單,易于解釋等優(yōu)點(diǎn),在變形分析與建模領(lǐng)域有著廣泛的應(yīng)用。然而,普通線性回歸分析所建立的模型是一種靜態(tài)模型,在實(shí)際應(yīng)用中,受結(jié)構(gòu)疲勞、材料腐蝕等因素的影響,變形體的結(jié)構(gòu)或物理性質(zhì)將隨著時(shí)間的推移或周邊環(huán)境的變化而發(fā)生改變,因此,利用普通線性回歸而建立的靜態(tài)模型難以進(jìn)行準(zhǔn)確的預(yù)報(bào)預(yù)測。為此,本文將變系數(shù)方法引入變形分析建模領(lǐng)域,主要研究內(nèi)容與成果如下： 1)對(duì)比分析了參數(shù)回歸分析、非參數(shù)回歸分析、半?yún)?shù)回歸分析、變系數(shù)回歸、時(shí)空回歸分析等回歸分析方法,并總結(jié)歸納了各方法的優(yōu)缺點(diǎn)。 2)利用變系數(shù)回歸分析方法進(jìn)行變形分析建模。在對(duì)變系數(shù)回歸算法進(jìn)行了深入分析的基礎(chǔ)上,采用局部線性估計(jì)的方法對(duì)變系數(shù)回歸中的系數(shù)進(jìn)行擬合。仿真和大壩變形建模實(shí)驗(yàn)表明：變系數(shù)模型中的系數(shù)可反映大壩結(jié)構(gòu)對(duì)外界影響因素響應(yīng)的變化,模型預(yù)測精度也明顯優(yōu)于普通的線性回歸模型。說明變系數(shù)回歸模型是一種動(dòng)態(tài)模型,適用于變形分析建模。 3)傳統(tǒng)的大壩變形分析建模中,常采用單測點(diǎn)模型建模。即模型僅對(duì)單一測點(diǎn)進(jìn)行建模分析,忽略了各測點(diǎn)之間的相關(guān)性,不利于對(duì)大壩整體性能狀態(tài)進(jìn)行判斷。本文提出了一種基于PCA(主成分分析)的時(shí)空變系數(shù)回歸方法,并利用該方法建立了時(shí)空變系數(shù)大壩變形模型。通過五強(qiáng)溪大壩上一條引張線上的變形監(jiān)測數(shù)據(jù)建模實(shí)驗(yàn)可知：該模型能夠準(zhǔn)確得出大壩上任意位置、任意時(shí)刻的變形位移量,并較之單測點(diǎn)模型及靜態(tài)時(shí)空變形模型具有更高的預(yù)測精度。圖24幅,表3個(gè),參考文獻(xiàn)60篇。
[Abstract]:Through the analysis of deformation monitoring data, it is a main work of deformation monitoring to find out the deformation law and establish a model to forecast and forecast the deformation.Among them, regression analysis, as a traditional deformation analysis method, has the advantages of simple modeling and easy interpretation, and is widely used in the field of deformation analysis and modeling.However, the model established by ordinary linear regression analysis is a static model, which is affected by structural fatigue, material corrosion and other factors in practical application.The structure or physical properties of deformable bodies will change with the passage of time or the change of surrounding environment. Therefore, it is difficult to predict accurately by the static model established by ordinary linear regression.Therefore, the variable coefficient method is introduced into the field of deformation analysis and modeling. The main research contents and results are as follows:1) the methods of regression analysis, such as parametric regression, non-parametric regression, semi-parametric regression, variable coefficient regression and space-time regression, are compared and analyzed, and the advantages and disadvantages of each method are summarized.2) the method of variable coefficient regression analysis is used to model the deformation analysis.Based on the deep analysis of variable coefficient regression algorithm, the local linear estimation method is used to fit the coefficients in variable coefficient regression.Simulation and dam deformation modeling experiments show that the coefficients in the variable coefficient model can reflect the response of the dam structure to the external factors, and the prediction accuracy of the model is obviously better than that of the ordinary linear regression model.It shows that the variable coefficient regression model is a kind of dynamic model, which is suitable for deformation analysis.3) in traditional dam deformation analysis modeling, single point model is often used.That is to say, the model can only model and analyze a single measuring point, neglecting the correlation between the measured points, which is not conducive to judging the overall performance state of the dam.In this paper, a spatio-temporal variable coefficient regression method based on PCA (principal component analysis) is proposed, and a dam deformation model with time-space variable coefficient is established by using this method.Through the modeling experiment of deformation monitoring data on a stretch line on the Wuqiangxi dam, it can be seen that the model can accurately obtain the deformation displacement at any position and at any time on the dam.Compared with the single point model and the static space-time deformation model, the prediction accuracy is higher.There are 24 figures, 3 tables and 60 references.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TU196.1;TV698.11

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 衛(wèi)建東;;現(xiàn)代變形監(jiān)測技術(shù)的發(fā)展現(xiàn)狀與展望[J];測繪科學(xué);2007年06期

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本文編號(hào)：1751322