發(fā)布時間：2018-09-04 16:32

【摘要】：大壩原型觀測數據處理是其安全監(jiān)控的重要研究內容，本文針對大壩安全監(jiān)控模型的擬合殘差、分量提取及指標擬定等內容，綜合運用統(tǒng)計學方法、遺傳算法、人工神經網絡、混沌理論、最小二乘支持向量機算法與有限元法等方法，以混凝土壩為研究對象，結合大壩位移原型觀測資料，在建立混凝土壩安全監(jiān)控模型的基礎上，研究了監(jiān)控模型的數值優(yōu)化方法，探究了大壩主要物理力學參數的反演方法，并給出了大壩變位預警指標的擬定方法。主要研究內容如下： (1)研究了混凝土壩安全監(jiān)控模型的構建方法，，利用遺傳算法優(yōu)化神經網絡算法，融合混沌理論，運用相空間重構等技術，對大壩位移擬合殘差進行預測，并將殘差預測項作為位移監(jiān)控模型的混沌因子，據此構建了考慮殘差混沌因子的混凝土壩位移混沌混合監(jiān)控模型，并驗證了所建模型的有效性。 (2)探討了混凝土壩綜合彈性模量的反演方法，在此基礎上，提出了一種能夠反映大壩位移和壩體彈性模量間非線性映射關系的最小二乘支持向量機(LS-SVM)反演算法，并利用MATLAB平臺，研制了基于LS-SVM算法的反分析程序。 (3)分析了服役期混凝土重力壩和拱壩的變形過程和轉異特征，并在對大壩正反分析的基礎上，進一步研究了混凝土壩變位預警指標的擬定方法。 (4)以某在役混凝土重力壩為例，在分析其水平位移變化規(guī)律的基礎上，基于上述理論與方法，構建了該壩位移統(tǒng)計模型、混合模型及考慮殘差混沌因子的混沌混合模型；并結合其典型壩段位移監(jiān)測資料及正反分析成果，擬定了該壩變位預警指標，為評判大壩安全狀態(tài)提供了理論依據。
[Abstract]:Dam prototype observation data processing is an important research content of dam safety monitoring. In this paper, the statistical method, genetic algorithm and artificial neural network are used synthetically to solve the problems of dam safety monitoring model, such as fitting residual, component extraction and index formulation, etc. Chaos theory, least square support vector machine (LS-SVM) and finite element method (FEM) are used to study concrete dam. Based on the observation data of dam displacement prototype, the safety monitoring model of concrete dam is established. The numerical optimization method of monitoring model is studied, the inversion method of the main physical and mechanical parameters of the dam is explored, and the method of drawing up the early warning index of dam displacement is given. The main research contents are as follows: (1) the construction method of concrete dam safety monitoring model is studied. The genetic algorithm is used to optimize the neural network algorithm, the chaos theory is fused, and the phase space reconstruction technology is used. The residual error of dam displacement fitting is predicted, and the residual prediction term is taken as the chaotic factor of displacement monitoring model. Based on this, the chaotic mixed monitoring model of displacement of concrete dam considering residual chaos factor is constructed. The validity of the model is verified. (2) the inversion method of composite elastic modulus of concrete dam is discussed. A least square support vector machine (LS-SVM) inversion algorithm which can reflect the nonlinear mapping relationship between dam displacement and elastic modulus of dam is proposed, and the MATLAB platform is used. The inverse analysis program based on LS-SVM algorithm is developed. (3) the deformation process and transition characteristics of concrete gravity dam and arch dam in service period are analyzed, and on the basis of positive and negative analysis of dam, In this paper, the method of determining early warning index for displacement of concrete dam is further studied. (4) taking a concrete gravity dam in service as an example, based on the above theory and method, the variation law of horizontal displacement is analyzed. The statistical model of displacement of the dam, the mixed model and the chaotic mixed model considering the residual chaos factor are constructed, and the displacement monitoring data of the typical dam segment and the positive and negative analysis results are combined to draw up the early warning index for the displacement of the dam. It provides a theoretical basis for judging dam safety state.
【學位授予單位】：南昌大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TV698.1

【引證文獻】

相關期刊論文 前1條

1 于君;;混凝土壩變形過程及監(jiān)控指標研究[J];水利規(guī)劃與設計;2016年02期

相關碩士學位論文 前1條

1 熊威;顧及多效應的混凝土壩位移聯(lián)合預報與監(jiān)控分析[D];南昌大學;2015年

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