發(fā)布時(shí)間：2018-11-19 11:02

【摘要】：橋梁工程是國家生命線工程。橋梁通車運(yùn)營后,隨著時(shí)間的推移,會(huì)受到環(huán)境侵蝕、人為因素、材料老化、自然災(zāi)害以及車輛荷載的交互作用,從而受到不同程度的損傷和劣化。橋梁健康監(jiān)測系統(tǒng)從傳感器實(shí)時(shí)監(jiān)測的數(shù)據(jù)中獲取結(jié)構(gòu)響應(yīng)信息和橋位環(huán)境狀況,并對(duì)數(shù)據(jù)計(jì)算分析,判斷和評(píng)估橋梁結(jié)構(gòu)的受力狀態(tài)和抗力衰減規(guī)律,確保橋梁安全運(yùn)營。健康監(jiān)測系統(tǒng)任務(wù)的核心是損傷識(shí)別,然而損傷識(shí)別的前提是建立精確的有限元模型。本文以橋梁健康監(jiān)測系統(tǒng)監(jiān)測的動(dòng)力響應(yīng)為有限元模型修正的研究目標(biāo),以神經(jīng)網(wǎng)絡(luò)算法和響應(yīng)面法在模型修正中的應(yīng)用為研究對(duì)象。采用ANSYS軟件對(duì)有限元模型進(jìn)行建模分析。利用遺傳算法優(yōu)化神經(jīng)網(wǎng)絡(luò),采用MATLAB編制算法程序?qū)崿F(xiàn)模型修正;同時(shí)利用MATLAB開發(fā)了工具箱,實(shí)現(xiàn)了響應(yīng)面法模型修正的可視化�；诮】当O(jiān)測系統(tǒng)實(shí)時(shí)監(jiān)測的數(shù)據(jù)可以實(shí)現(xiàn)模型修正的實(shí)時(shí)性,能夠及時(shí)的為橋梁結(jié)構(gòu)計(jì)算提供一個(gè)準(zhǔn)確的有限元模型。主要研究內(nèi)容如下:1、介紹有限元模型修正的理論,對(duì)有限元模型修正的一般過程進(jìn)行說明;對(duì)模型修正中的參數(shù)選取,以及模型修正后的誤差判別做了詳細(xì)介紹;2、針對(duì)傳統(tǒng)加速度傳感器優(yōu)化布置方法的不足,提出了本文方法,結(jié)合工程經(jīng)驗(yàn)完成沮河大橋加速度傳感器的優(yōu)化布置;總結(jié)不同傳感器性能的優(yōu)缺點(diǎn),并介紹全橋傳感器的布置方案;3、針對(duì)普通廣義回歸神經(jīng)網(wǎng)絡(luò)的缺陷,提出基于遺傳算法的優(yōu)化方法,并將其應(yīng)用到有限元模型修正中,詳細(xì)介紹神經(jīng)網(wǎng)絡(luò)在模型修正中應(yīng)用原理及流程;4、在MATLAB軟件環(huán)境下,將本文提出的優(yōu)化廣義回歸神經(jīng)網(wǎng)絡(luò)模型修正方法運(yùn)用到依托工程上。通過對(duì)修正結(jié)果的對(duì)比分析,驗(yàn)證了基于遺傳算法優(yōu)化神經(jīng)網(wǎng)絡(luò)模型修正法的準(zhǔn)確性、優(yōu)越性和有效性;5、介紹響應(yīng)面法進(jìn)行模型修正的原理及流程,并將其應(yīng)用到依托工程上。利用MATLAB開發(fā)出一個(gè)工具箱,將響應(yīng)面法模型修正做到了可視化。
[Abstract]:Bridge engineering is a national lifeline project. After the bridge is opened to traffic, with the passage of time, it will be affected by environmental erosion, human factors, material aging, natural disasters and the interaction of vehicle loads, so it will be damaged and degraded to varying degrees. The bridge health monitoring system acquires the structure response information and the bridge environment condition from the sensor real-time monitoring data, calculates and analyzes the data, judges and evaluates the stress state and the resistance attenuation rule of the bridge structure, and ensures the bridge safe operation. Damage identification is the core of the task of health monitoring system. However, the premise of damage identification is to establish an accurate finite element model. In this paper, the dynamic response of bridge health monitoring system is taken as the research object of finite element model modification, and the application of neural network algorithm and response surface method in model modification is studied. The finite element model is modeled and analyzed by ANSYS software. Genetic algorithm is used to optimize neural network, MATLAB is used to program the model correction, and MATLAB is used to develop toolbox to realize the visualization of response surface model modification. The real-time monitoring data based on the health monitoring system can realize the real-time correction of the model and can provide an accurate finite element model for bridge structure calculation in time. The main research contents are as follows: 1. The theory of finite element model modification is introduced and the general process of finite element model modification is explained. 2. Aiming at the deficiency of the traditional acceleration sensor optimization arrangement method, this paper puts forward the method of this paper, combining the engineering experience to complete the optimization layout of the acceleration sensor of the Ju River Bridge; The advantages and disadvantages of different sensors are summarized, and the layout scheme of the full bridge sensor is introduced. 3. Aiming at the defects of general generalized regression neural network, an optimization method based on genetic algorithm is proposed and applied to finite element model modification. The application principle and flow chart of neural network in model modification are introduced in detail. 4. In the MATLAB software environment, the optimized generalized regression neural network model modification method proposed in this paper is applied to the supporting engineering. The accuracy, superiority and effectiveness of the neural network model modification method based on genetic algorithm are verified by the comparison and analysis of the modified results. 5. The principle and process of model modification based on response surface method are introduced, and applied to supporting engineering. A toolbox is developed by using MATLAB to visualize the response surface model modification.
【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U446;U441

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