【摘要】：變形監(jiān)測(cè)是監(jiān)測(cè)變形體是否安全的一種重要手段，具體過(guò)程是通過(guò)對(duì)變形體的監(jiān)測(cè)取得原始變形數(shù)據(jù)，然后對(duì)變形數(shù)據(jù)進(jìn)行分析處理，判斷出變形體的安全狀況。在初期蓄水和長(zhǎng)期運(yùn)行中，大壩都存在著發(fā)生事故的可能性。大壩一旦出現(xiàn)異常狀態(tài)，必須及時(shí)發(fā)現(xiàn)和處理，否則可能導(dǎo)致嚴(yán)重后果。但是對(duì)于大壩類型監(jiān)測(cè)對(duì)象的變形分析，除了要關(guān)注過(guò)去的變形狀態(tài)，還應(yīng)該預(yù)測(cè)變形體將來(lái)可能發(fā)生的變形，實(shí)時(shí)監(jiān)控變形體的安全狀況，，做到安全事故的提前預(yù)防，這就需要建立監(jiān)測(cè)體變形預(yù)測(cè)模型。變形監(jiān)測(cè)模型是對(duì)已有的變形監(jiān)測(cè)數(shù)據(jù)結(jié)果做統(tǒng)計(jì)分析，找出數(shù)據(jù)間的規(guī)律性，利用建模工具建立監(jiān)測(cè)模型，從而達(dá)到預(yù)測(cè)出下一周期的可能的形變量。 本文以港口灣水庫(kù)大壩的形變?yōu)樽冃伪O(jiān)測(cè)對(duì)象，獲取了2003年至2012年各個(gè)年度不同監(jiān)測(cè)周期的大量變形監(jiān)測(cè)數(shù)據(jù)，以具有強(qiáng)大的數(shù)值計(jì)算和仿真功能的Matlab軟件為工具，采用極限誤差法和拉格朗日插值方法對(duì)原始監(jiān)測(cè)數(shù)據(jù)進(jìn)行預(yù)處理，然后根據(jù)灰色系統(tǒng)分析理論和人工神經(jīng)網(wǎng)絡(luò)理論，分別以月度、季度、年度為周期整理原始數(shù)據(jù)，建立GM(1，1)模型和BP神經(jīng)網(wǎng)絡(luò)模型進(jìn)行預(yù)報(bào)和分析，與港口灣水庫(kù)大壩實(shí)際情況相結(jié)合分析模型的可靠性，得出一些適用于港口灣水庫(kù)大壩安全監(jiān)測(cè)的結(jié)論。
[Abstract]:Deformation monitoring is an important means to monitor the safety of deformable body. The concrete process is to obtain the original deformation data by monitoring the deformable body, and then analyze and process the deformation data to judge the safety condition of the deformable body. In the initial water storage and long-term operation, the dam has the possibility of accidents. In case of abnormal state of dam, it must be detected and dealt with in time, otherwise it may lead to serious consequences. But for the deformation analysis of dam type monitoring object, in addition to paying attention to the deformation state in the past, we should also predict the deformation that may occur in the future, monitor the safety condition of the deformable body in real time, and prevent the safety accident in advance. Therefore, it is necessary to establish the model of deformation prediction for monitoring body. The deformation monitoring model is to make statistical analysis of the existing deformation monitoring data, to find out the regularity between the data, and to establish the monitoring model by using modeling tools, so as to predict the possible shape variables of the next cycle. In this paper, the deformation of the dam of Port Bay Reservoir is taken as the object of deformation monitoring, and a large number of deformation monitoring data of different monitoring periods from 2003 to 2012 are obtained. The Matlab software, which has a powerful function of numerical calculation and simulation, is used as a tool. The limit error method and Lagrange interpolation method are used to preprocess the original monitoring data. According to the grey system analysis theory and the artificial neural network theory, the original data are sorted out in monthly, quarterly and annual periods, respectively. The GM (1 + 1) model and BP neural network model are established for prediction and analysis. The reliability of the model is analyzed in combination with the actual situation of the port bay reservoir dam, and some conclusions suitable for dam safety monitoring of the port bay reservoir are obtained.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TV698.1

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