【摘要】：雙曲拱橋是我國獨有的、極具名族氣息與特點的橋型，也是上世紀60~70年代建設最多的一種拱橋型式。由于這種結構型式所采用的積木式拼裝組合構造型式及低配筋構造使其結構整體性先天不足，加之設計、施工存在先天缺陷，在自然環(huán)境及超負荷交通量情況下，在役雙曲拱橋存在不同程度的損傷。為了保障交通的順暢，了解橋梁的實際工作狀態(tài)（損傷狀況、實際承載力等），須對既有雙曲拱橋的現(xiàn)實工作狀況做出科學的評估。本論文基于RBF神經(jīng)網(wǎng)絡對雙曲拱橋初始有限元模型進行修正，建立反映在役雙曲拱橋實際狀況的有限元模型，基于修正后的有限元模型進行全橋控制截面承載能力系數(shù)評估和裸拱極限承載能力評估。 本文以淌溝大橋為背景，運用RBF神經(jīng)網(wǎng)絡對初始有限元模型進行修正，以修正后的有限元模型為基準進行橋梁承載力評估。論文主要工作如下： 1、對在役雙曲拱橋進行外觀調(diào)查，根據(jù)《公路橋梁技術狀況評定標準》、《公路橋梁承載力評定規(guī)程》進行橋梁綜合評定。將實際拱軸線及影響雙曲拱橋承載能力的病害在有限元模型中充分考慮，從而達到模型修正的目的。進行實橋現(xiàn)場靜載實驗，提取合理實驗工況及實驗截面進行后面神經(jīng)網(wǎng)絡靜力優(yōu)化樣本確定。 2、進行參數(shù)靈敏度分析，選取對結構靜力特征響應量（撓度）有顯著影響的設計參數(shù)作為待修正設計參數(shù)。確定待修正參數(shù)的優(yōu)化空間，基于均勻設計理論合理選取神經(jīng)網(wǎng)絡訓練樣本進行神經(jīng)網(wǎng)絡訓練�；谟柧毢蟮木W(wǎng)絡，利用RBF神經(jīng)網(wǎng)絡的泛化特性，求出設計參數(shù)的目標值即待修正參數(shù)的實際值。為了驗證徑向基神經(jīng)網(wǎng)絡的修正性能，，采用ANSYS自帶的一階優(yōu)化算法進行有限元模型修正，進行兩者的結果對比分析，驗證基于徑向基神經(jīng)網(wǎng)絡的可行性及實用性。 3、以修正后的橋梁有限元模型為基準從截面的真實強度、恒載與活載效應、結構損傷三方面進行橋梁承載力系數(shù)計算�？紤]拱肋彈性模量折減、拱肋有效面積折減及超載計算結構控制截面的承載力系數(shù)從而綜合的評價橋梁承載力�；跇O限承載力方法驗算雙曲拱橋裸拱在各種荷載組合下的極限承載力。
[Abstract]:The hyperbolic arch bridge is a unique bridge type with unique and famous atmosphere and characteristics in our country. It is also one of the most arched bridges built in the 1960s and 1970s. Because of the structural integrity of the building block assembly and combination structure and the low reinforcement structure, and because of the inherent defects in the design, the natural environment and the overloaded traffic volume are in the condition of the natural environment and the overload of traffic. In service, the hyperbolic arch bridge has different degree of damage. In order to ensure the smooth traffic and understand the actual working state of the bridge (damage condition, actual bearing capacity, etc.), it is necessary to make a scientific evaluation of the actual working condition of the existing hyperbolic arch bridge. In this paper, the initial finite element model of hyperbolic arch bridge is modified based on RBF neural network, and a finite element model is established to reflect the actual situation of the hyperbolic arch bridge in service. Based on the modified finite element model, the load capacity coefficient of the control section of the bridge and the ultimate bearing capacity of the bare arch are evaluated. In this paper, the initial finite element model is modified by RBF neural network, and the bridge bearing capacity is evaluated based on the modified finite element model. The main work of this paper is as follows: 1. The external appearance of the double-curved arch bridge in service is investigated, and the comprehensive evaluation of the bridge bearing capacity is carried out according to the Evaluation Standard of the Technical condition of the Highway Bridge and the rules for the Evaluation of the bearing capacity of the Highway Bridge. The actual arch axis and the diseases affecting the bearing capacity of hyperbolic arch bridge are fully considered in the finite element model so as to achieve the purpose of model modification. The static load experiment of the real bridge was carried out, and the reasonable experimental conditions and the experimental section were extracted to determine the static optimization samples of the back neural network. 2. The parameter sensitivity analysis is carried out, and the design parameters which have significant influence on the static response (deflection) of the structure are selected as the design parameters to be modified. The optimization space of the parameters to be modified is determined, and the neural network training samples are reasonably selected based on uniform design theory for neural network training. Based on the trained network and the generalization characteristic of the RBF neural network, the target value of the design parameters is obtained, that is, the actual value of the parameters to be modified. In order to verify the modification performance of radial basis function neural network, the first order optimization algorithm of ANSYS is used to modify the finite element model, and the results are compared and analyzed. The feasibility and practicability of radial basis function neural network based on radial basis function neural network are verified. 3. Based on the modified finite element model of the bridge, the bearing capacity coefficient of the bridge is calculated from three aspects: the true strength of the section, the effect of dead load and live load, and the damage of the structure. Considering the reduction of elastic modulus of arch rib, the reduction of effective area of arch rib and the calculation of bearing capacity coefficient of control section of structure under overload, the bearing capacity of bridge is evaluated synthetically. The ultimate bearing capacity of bare arch of hyperbolic arch bridge under various load combinations is verified based on ultimate bearing capacity method.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U441;U448.221

【參考文獻】

相關期刊論文 前8條

1 瞿偉廉,譚冬梅,汪菁,滕軍;基于神經(jīng)網(wǎng)絡的大型空間網(wǎng)架結構的有限元模型修正[J];地震工程與工程振動;2003年04期

2 宗周紅,任偉新,鄭振飛;既有橋梁承載能力評估方法[J];地震工程與工程振動;2005年05期

3 趙大亮;李愛群;繆長青;王浩;;既有公路橋梁評估方法研究現(xiàn)狀[J];工程抗震與加固改造;2007年01期

4 胡志堅;賈麗君;肖汝誠;袁帥華;;既有橋梁技術狀態(tài)評估研究現(xiàn)狀與展望[J];國防交通工程與技術;2006年02期

5 王有志,徐鴻儒,任鋒;鋼筋混凝土梁式橋的安全性評估系統(tǒng)研究與開發(fā)[J];公路交通科技;2002年01期

6 單德山;丁德豪;李喬;黃珍;;基于RBF神經(jīng)網(wǎng)絡的單塔斜拉橋模型修正[J];重慶交通大學學報(自然科學版);2013年04期

7 高懷志,王君杰;橋梁檢測和狀態(tài)評估研究與應用[J];世界地震工程;2000年02期

8 胡大琳，陳祥寶;拱式拱上結構鋼筋混凝土拱橋極限承載力分析[J];西安公路交通大學學報;1994年04期

本文編號：2371833