本文關(guān)鍵詞：基于動應(yīng)變數(shù)據(jù)的橋梁移動荷載識別研究　出處：《武漢理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 移動荷載識別 有限元修正 瞬態(tài)分析 神經(jīng)網(wǎng)絡(luò) ANSYS

【摘要】：監(jiān)測橋上移動車輛荷載，明確其車輛參數(shù)對橋梁結(jié)構(gòu)可靠度設(shè)計以及運(yùn)營維護(hù)管理等方面都具有重要意義。目前的移動荷載識別理論應(yīng)用于實際橋梁的技術(shù)尚未成熟，現(xiàn)有的車輛稱重有成本高等局限性，因此探究出一種簡單、快速、有效的移動荷載識別方法具有非常重要的意義。 本文利用大型有限元程序ANSYS的APDL語言建立橋梁結(jié)構(gòu)參數(shù)化初始有限元模型，結(jié)合實橋靜載試驗數(shù)據(jù)對初始模型進(jìn)行有限元靜力修正。運(yùn)用ANSYS對修正后的有限元模型進(jìn)行移動荷載的動力瞬態(tài)分析，對不同車重、不同車速、不同橫向位置進(jìn)行計算分析，提取應(yīng)變傳感器測點(diǎn)的計算結(jié)果，采用分階段識別方法首先識別出車道位置和車速，最后利用計算結(jié)果建立車重與車道位置、車速及各測點(diǎn)動應(yīng)變峰值對應(yīng)關(guān)系的神經(jīng)網(wǎng)絡(luò)訓(xùn)練樣本，采用BP神經(jīng)網(wǎng)絡(luò)的算法利用各測點(diǎn)的動應(yīng)變峰值數(shù)據(jù)和已識別出的參數(shù)進(jìn)行移動荷載車重識別，并分別建立了一個模擬模型和實橋?qū)@種方法進(jìn)行探究。具體研究工作和主要成果如下： （1）利用現(xiàn)場橋梁檢測和荷載試驗靜載數(shù)據(jù)對有限元模型進(jìn)行修正，選取各構(gòu)件抗彎剛度為修正參數(shù)，，建立合適目標(biāo)函數(shù)，比較研究不同的優(yōu)化算法，運(yùn)用一階尋優(yōu)法得到滿意的修正結(jié)果。 （2）模擬車輛荷載在橋梁上移動過程并考慮到動力作用影響，基于修正后的有限元模型，利用ANSYS的APDL語言編制程序模擬移動荷載并對其做動力瞬態(tài)分析。 （3）探究出利用不同車道對應(yīng)固定的唯一的各T梁底部動應(yīng)變峰值大小順序的規(guī)律來進(jìn)行車道識別的方法；探究出通過找出不同測試截面的動應(yīng)變峰值或峰值區(qū)域中心對應(yīng)的時間點(diǎn)的差值與其對應(yīng)的橋梁實際的距離的比值的平均值來進(jìn)行車速識別的方法，并驗證了該方法的有效性。 （4）探究出利用不同移動荷載參數(shù)作用下各測點(diǎn)的動應(yīng)變響應(yīng)值，建立神經(jīng)網(wǎng)絡(luò)訓(xùn)練樣本，利用訓(xùn)練后的BP神經(jīng)網(wǎng)絡(luò)識別車重的方法。 （5）選取了廣西金鯉水泥有限公司專用碼頭作為工程實例，對其進(jìn)行驗證行試驗，試驗結(jié)果表明：本文的基于動應(yīng)變數(shù)據(jù)的橋梁移動荷載識別方法是有效的。
[Abstract]:Monitor the load of moving vehicles on the bridge. It is very important to clarify the vehicle parameters for the reliability design of bridge structure and the operation and maintenance management. The current technology of mobile load identification theory applied to practical bridge is not mature. The existing vehicle weighing has the limitation of high cost, so it is of great significance to explore a simple, fast and effective method for identification of moving load. In this paper, the parametric initial finite element model of bridge structure is established by using the APDL language of the large finite element program ANSYS. Combined with the static load test data of the real bridge, the initial model is modified by finite element static force. The dynamic transient analysis of the modified finite element model is carried out by using ANSYS, and different vehicle weights and different speeds are obtained. Different lateral positions are calculated and analyzed, the results of strain sensor measurement points are extracted, and the lane position and speed are identified by using the method of phased identification. Finally, the vehicle weight and lane position are established by using the calculation results. The training samples of the corresponding relationship between the velocity and the peak dynamic strain of each measuring point are trained by neural network. The BP neural network algorithm is used to identify the moving load vehicle weight using the dynamic strain peak data of each measuring point and the identified parameters. A simulation model and a real bridge are established to explore this method. The specific research work and main results are as follows: 1) the finite element model is modified by static load data of in-situ bridge detection and load test. The bending stiffness of each component is selected as the correction parameter, and the appropriate objective function is established, and different optimization algorithms are compared and studied. The first order optimization method is used to obtain satisfactory correction results. 2) simulating the moving process of vehicle load on the bridge and considering the influence of dynamic action, based on the modified finite element model. The moving load is simulated by APDL language of ANSYS and the dynamic transient analysis is done. 3) exploring the method of lane identification by using the law of the order of peak value of dynamic strain at the bottom of each T beam corresponding to fixed different lanes. This paper explores the method of speed identification by finding out the average value of the difference between the difference between the peak value of dynamic strain or the center of the peak region of different test sections and the ratio of the actual distance of the bridge corresponding to the difference between the difference of the time point and the actual distance of the bridge. The effectiveness of the method is verified. (4) the method of using the dynamic strain response value of each measuring point under the action of different moving load parameters to establish the training sample of neural network and identify the vehicle weight by BP neural network after training is explored. The special wharf of Guangxi Jinli cement Co., Ltd is selected as an engineering example, and the test is carried out. The experimental results show that the method of bridge moving load identification based on dynamic strain data in this paper is effective.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U446

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