本文選題：有限元模型修正 + 徑向基神經(jīng)網(wǎng)絡��； 參考：《合肥工業(yè)大學》2015年碩士論文

【摘要】：跟隨著有限元技術的深入發(fā)展,有限元模型修正技術在過去的20多年也得到了廣泛研究及應用。目前流行的有限元模型參數(shù)型修正法通過對模型參數(shù)的選取與修正,使模型結(jié)構(gòu)響應與實際結(jié)構(gòu)響應趨于一致,從而建立能夠反映以及預測實際結(jié)構(gòu)行為的有限元模型。在土木工程領域,由于針對結(jié)構(gòu)建立有限元模型時基于一定的假設,以及實際結(jié)構(gòu)在建設和運營中受外界荷載及環(huán)境的影響,有限元模型與實際結(jié)構(gòu)存在誤差,該模型通常不能夠反映結(jié)構(gòu)的實際工作狀態(tài)。因此,為了確保模型計算結(jié)果能夠表達實際結(jié)構(gòu)狀況,應對其進行有限元模型修正。近年來,大跨橋梁結(jié)構(gòu)的健康監(jiān)測與安全狀態(tài)評估成為了國內(nèi)外的熱點研究領域,而有限元模型修正技術能夠為此提供一個可靠且實效的模型來反演橋梁結(jié)構(gòu)的工作及健康狀況甚至識別結(jié)構(gòu)損傷部位與程度等等。因此,有限元修正技術具有重要的工程研究意義。全文研究內(nèi)容及安排如下：(1)回顧有限元模型修正技術發(fā)展歷程,歸納總結(jié)有限元模型修正方法,分析各種方法優(yōu)缺點。(2)利用徑向基函數(shù)神經(jīng)網(wǎng)絡(radial basis function, RBF)進行有限元模型修正,并結(jié)合有限元模型修正問題的特點,對網(wǎng)絡輸入輸出參數(shù)進行分析,選擇構(gòu)建RBF網(wǎng)絡結(jié)構(gòu)參數(shù),確定徑向基神經(jīng)網(wǎng)絡修正有限元模型以及確定施工索力的技術路線。(3)通過假設既有狀態(tài),利用RBF神經(jīng)網(wǎng)絡對一預應力混凝土簡支小箱梁有限元模型進行修正,從而驗證該方法的有效性。(4)利用RBF神經(jīng)網(wǎng)絡確定系桿拱橋吊桿施工索力張拉,完成有限元模型修正擴展應用。(5)根據(jù)圖紙采用梁格法建立梁拱組合體系橋梁,首先利用主成分分析法對網(wǎng)絡訓練樣本進行預處理網(wǎng)絡輸出輸入樣本對,然后根據(jù)實測橋梁結(jié)構(gòu)響應數(shù)據(jù),利用訓練檢驗后的RBF神經(jīng)網(wǎng)絡進行有限元模型修正。修正后有限元模型計算值與實測值之間誤差較原有設計模型有很大程度改進。
[Abstract]:With the further development of finite element technology, finite element model modification technology has been widely studied and applied in the past 20 years. By selecting and modifying the parameters of finite element model, the structural response of the model is consistent with the response of the actual structure, and the finite element model which can reflect and predict the behavior of the actual structure is established. In the field of civil engineering, because the finite element model is based on certain assumptions, and the actual structure is affected by the external load and environment in the construction and operation, there are errors between the finite element model and the actual structure. The model does not usually reflect the actual working state of the structure. Therefore, in order to ensure that the model results can express the actual structure, it is necessary to modify the finite element model. In recent years, health monitoring and safety state assessment of long-span bridge structures have become a hot research field at home and abroad. The finite element model modification technique can provide a reliable and effective model for retrieving the work and health of bridge structure and even identifying the damage location and degree of the structure and so on. Therefore, the finite element correction technique has important engineering significance. The contents and arrangements of this paper are as follows: (1) reviewing the development of finite element model modification technology, summarizing the finite element model modification methods, analyzing the advantages and disadvantages of each method. 2) using radial basis function neural network radial basis function, RBF) to modify the finite element model. Combined with the characteristics of the finite element model correction problem, the input and output parameters of the network are analyzed, and the parameters of the RBF network structure are selected. Determining the modified finite element model of radial basis function neural network and determining the technical route of cable force in construction. The finite element model of a prestressed concrete simply supported small box girder is modified by RBF neural network. Therefore, the validity of the method is verified. The RBF neural network is used to determine the cable tension in the construction of tied arch bridge, and the finite element model modification and extension application. 5) according to the drawing, the beam lattice method is used to establish the beam arch composite system bridge. Firstly, the network training samples are preprocessed by principal component analysis (PCA), and then the finite element model is modified by using the RBF neural network after training and checking according to the measured bridge structure response data. The error between the calculated value and the measured value of the modified finite element model is much improved than that of the original design model.
【學位授予單位】：合肥工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U441;U448.225

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