基于RBF神經(jīng)網(wǎng)絡(luò)靜力有限元模型修正的雙曲拱橋承載力評(píng)估
發(fā)布時(shí)間：2018-12-11 04:06
【摘要】：雙曲拱橋是我國(guó)獨(dú)有的、極具名族氣息與特點(diǎn)的橋型，也是上世紀(jì)60~70年代建設(shè)最多的一種拱橋型式。由于這種結(jié)構(gòu)型式所采用的積木式拼裝組合構(gòu)造型式及低配筋構(gòu)造使其結(jié)構(gòu)整體性先天不足，加之設(shè)計(jì)、施工存在先天缺陷，在自然環(huán)境及超負(fù)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(shí)際工作狀態(tài)（損傷狀況、實(shí)際承載力等），須對(duì)既有雙曲拱橋的現(xiàn)實(shí)工作狀況做出科學(xué)的評(píng)估。本論文基于RBF神經(jīng)網(wǎng)絡(luò)對(duì)雙曲拱橋初始有限元模型進(jìn)行修正，建立反映在役雙曲拱橋?qū)嶋H狀況的有限元模型，基于修正后的有限元模型進(jìn)行全橋控制截面承載能力系數(shù)評(píng)估和裸拱極限承載能力評(píng)估。 本文以淌溝大橋?yàn)楸尘�，運(yùn)用RBF神經(jīng)網(wǎng)絡(luò)對(duì)初始有限元模型進(jìn)行修正，以修正后的有限元模型為基準(zhǔn)進(jìn)行橋梁承載力評(píng)估。論文主要工作如下： 1、對(duì)在役雙曲拱橋進(jìn)行外觀調(diào)查，根據(jù)《公路橋梁技術(shù)狀況評(píng)定標(biāo)準(zhǔn)》、《公路橋梁承載力評(píng)定規(guī)程》進(jìn)行橋梁綜合評(píng)定。將實(shí)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(dá)到模型修正的目的。進(jìn)行實(shí)橋現(xiàn)場(chǎng)靜載實(shí)驗(yàn)，提取合理實(shí)驗(yàn)工況及實(shí)驗(yàn)截面進(jìn)行后面神經(jīng)網(wǎng)絡(luò)靜力優(yōu)化樣本確定。 2、進(jìn)行參數(shù)靈敏度分析，選取對(duì)結(jié)構(gòu)靜力特征響應(yīng)量（撓度）有顯著影響的設(shè)計(jì)參數(shù)作為待修正設(shè)計(jì)參數(shù)。確定待修正參數(shù)的優(yōu)化空間，基于均勻設(shè)計(jì)理論合理選取神經(jīng)網(wǎng)絡(luò)訓(xùn)練樣本進(jìn)行神經(jīng)網(wǎng)絡(luò)訓(xùn)練�；谟�(xùn)練后的網(wǎng)絡(luò)，利用RBF神經(jīng)網(wǎng)絡(luò)的泛化特性，求出設(shè)計(jì)參數(shù)的目標(biāo)值即待修正參數(shù)的實(shí)際值。為了驗(yàn)證徑向基神經(jīng)網(wǎng)絡(luò)的修正性能，，采用ANSYS自帶的一階優(yōu)化算法進(jìn)行有限元模型修正，進(jìn)行兩者的結(jié)果對(duì)比分析，驗(yàn)證基于徑向基神經(jīng)網(wǎng)絡(luò)的可行性及實(shí)用性。 3、以修正后的橋梁有限元模型為基準(zhǔn)從截面的真實(shí)強(qiáng)度、恒載與活載效應(yīng)、結(jié)構(gòu)損傷三方面進(jìn)行橋梁承載力系數(shù)計(jì)算�？紤]拱肋彈性模量折減、拱肋有效面積折減及超載計(jì)算結(jié)構(gòu)控制截面的承載力系數(shù)從而綜合的評(píng)價(jià)橋梁承載力。基于極限承載力方法驗(yàn)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(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U441;U448.221
本文編號(hào)：2371833
[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(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U441;U448.221
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