發(fā)布時間：2018-03-11 17:27

  本文選題：大壩變形　切入點：基因表達式編程　出處：《江西理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著大壩變形監(jiān)測技術(shù)的發(fā)展，大壩變形監(jiān)測數(shù)據(jù)的獲取也越來越多樣化。但是其數(shù)據(jù)分析方法仍然是針對單點的預(yù)測，無法從整體的角度對大壩的變形進行分析。人們迫切需要一種可以從宏觀的角度快速分析大壩整體變形規(guī)律的方法。由此可見，對大壩進行位移強度聚類分析，找出大壩的整體變形規(guī)律具有重要的意義。針對這種情況，本文將改進的基因表達式自動聚類算法應(yīng)用于大壩位移強度的聚類分析中，主要研究工作如下： （1）對引入的位移強度進行了深入的分析，結(jié)合大壩變形監(jiān)測數(shù)據(jù)的特點，選擇大壩所有變形監(jiān)測點在監(jiān)測時段內(nèi)的平均位移速率作為臨界速率，并以此為基準確定大壩的位移強度值； （2）針對大壩變形監(jiān)測數(shù)據(jù)所含的突變的噪聲可能對聚類分析產(chǎn)生不利的影響，采用小波閾值去噪算法對原始的大壩變形監(jiān)測數(shù)據(jù)進行噪聲的去除； （3）為了消除大壩監(jiān)測點分布不均及監(jiān)測點之間較大的空間間隔所導(dǎo)致的大壩整體變形規(guī)律分析中存在分析的盲點，在壩體表面生成1m*1m的空間格網(wǎng)均勻的覆蓋壩體，采用徑向基插值算法對格網(wǎng)點進行位移強度插值，以此反映大壩的整體變形情況； （4）針對現(xiàn)有的基因表達式自動聚類算法在高維度空間數(shù)據(jù)聚類方面的不足，對現(xiàn)有的算法進行了改進。提出基于主成分的基因表達式自動聚類算法，并將高維度的大壩變形監(jiān)測數(shù)據(jù)映射到低維度空間，通過降維實現(xiàn)大壩變形監(jiān)測數(shù)據(jù)的聚類操作； （5）采用.NET與Matlab混合編程技術(shù)，實現(xiàn)所提出的基于基因表達編程的大壩位移強度聚類分析模型； （6）將基于基因表達式編程的大壩位移強度聚類分析模型應(yīng)用于江西省贛州市上猶江水庫大壩的變形監(jiān)測工程中，并對該大壩的整體變形規(guī)律進行過分析。 通過分析可以清楚地看出，，上猶江水庫大壩壩體下游的位置形變活動比較穩(wěn)定，上游的位置變形活動比較劇烈，中間位置形變活動居于二者之間；大壩的左岸變形活動比較穩(wěn)定，右岸變形活動比較頻繁；且水流方向的形變活動要強于大壩軸線方向和垂直位移方向。將位移強度聚類可視化效果圖與各個方向的位移強度值三維效效圖進行比較分析，可以發(fā)現(xiàn)聚類的結(jié)果與大壩的變形情況基本上是相符的，由此也證明了所提出的基于基因表達式編程的大壩位移強度聚類分析模型的聚類分析結(jié)果基本上是可靠的。
[Abstract]:With the development of dam deformation monitoring technology, the acquisition of dam deformation monitoring data is becoming more and more diversified. It is not possible to analyze the deformation of the dam from the whole angle. People urgently need a method to analyze the deformation law of the dam quickly from the macro angle. Thus, the displacement intensity cluster analysis of the dam is carried out. It is very important to find out the global deformation law of the dam. In this paper, the improved gene expression automatic clustering algorithm is applied to the clustering analysis of the dam displacement intensity. The main research work is as follows:. 1) based on the analysis of the displacement intensity introduced, combined with the characteristics of the dam deformation monitoring data, the average displacement rate of all the dam deformation monitoring points during the monitoring period is selected as the critical rate. The displacement strength of the dam is determined by this criterion. 2) aiming at the sudden noise in dam deformation monitoring data, the wavelet threshold de-noising algorithm is used to remove the noise from the original dam deformation monitoring data. 3) in order to eliminate the blind spot in the analysis of dam deformation law caused by the uneven distribution of monitoring points and the large space interval between the monitoring points, the dam body is uniformly covered by a space grid of 1mmm or 1m on the surface of the dam. The radial basis interpolation algorithm is used to interpolate the displacement intensity of the lattice dot to reflect the whole deformation of the dam. 4) aiming at the deficiency of the existing automatic clustering algorithm of gene expression in high-dimensional spatial data clustering, this paper improves the existing algorithm, and proposes an automatic clustering algorithm of gene expression based on principal component. The high-dimensional dam deformation monitoring data are mapped to the low-dimensional space, and the clustering operation of dam deformation monitoring data is realized by reducing the dimension. In this paper, the hybrid programming technology of .NET and Matlab is used to realize the dam displacement intensity cluster analysis model based on gene expression programming. The model of dam displacement intensity cluster analysis based on genetic expression programming is applied to the dam deformation monitoring project of Shangyou River Reservoir in Ganzhou City Jiangxi Province and the whole deformation law of the dam is analyzed. It can be clearly seen from the analysis that the deformation activity in the lower reaches of the dam body of Upper Jujiang Reservoir is relatively stable, the deformation activity in the upper reaches is more intense, and the deformation activity in the middle position is between the two. The deformation activity of the left bank of the dam is relatively stable, and the deformation activity of the right bank is more frequent. The deformation activity in the direction of flow is stronger than that in the direction of the axis and vertical displacement of the dam. The visual effect map of displacement intensity clustering is compared with the three dimensional effect chart of displacement intensity in each direction. It can be found that the clustering results are basically consistent with the deformation of the dam, which also proves that the clustering analysis results of the proposed clustering model of dam displacement strength based on genetic expression programming are basically reliable.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TV698.11

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