本文選題：橋梁結(jié)構(gòu)　切入點：損傷識別　出處：《吉林大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：橋梁結(jié)構(gòu)最重要的交通基礎(chǔ)設(shè)施結(jié)構(gòu)形式，在道路建設(shè)中廣泛應(yīng)用。橋梁災(zāi)害事故的發(fā)生導(dǎo)致巨大的人員傷亡及經(jīng)濟損失，造成了惡劣的社會影響。災(zāi)害事故的發(fā)生表明橋梁結(jié)構(gòu)在不可見的內(nèi)在損傷作用下，如果不及時進行檢測、維修或加固，，容易導(dǎo)致垮塌事故的發(fā)生。因此，對橋梁結(jié)構(gòu)進行及時科學(xué)的損傷檢測和評估，及時了解結(jié)構(gòu)損傷狀況，對于保證結(jié)構(gòu)安全運營，避免災(zāi)害事故發(fā)生意義深遠(yuǎn)。 本文以廣泛使用的簡支梁橋和連續(xù)梁橋為研究對象，開展了基于結(jié)構(gòu)動力特性損傷識別指標(biāo)的優(yōu)選工作；在此基礎(chǔ)上，提出了相應(yīng)的損傷程度識別方法。主要工作如下： （1）基于動力特性損傷識別理論基礎(chǔ)，對比分析了頻率、振型、模態(tài)曲率、模態(tài)柔度、均布荷載面曲率等指標(biāo)在損傷位置識別方面的敏感性。識別結(jié)果表明，模態(tài)曲率差、模態(tài)柔度差曲率以及均勻荷載面曲率差等指標(biāo)能夠?qū)崿F(xiàn)有效的損傷定位。 （2）對模態(tài)柔度差曲率、模態(tài)曲率差、均勻荷載面曲率差的抗噪聲能力進行分析，結(jié)果表明模態(tài)柔度差曲率具有較強的抗噪聲干擾能力，可以作為結(jié)構(gòu)損傷識別的優(yōu)選指標(biāo)，并將該指標(biāo)應(yīng)用于連續(xù)梁橋的損傷識別中。 （3）以簡支梁橋和連續(xù)梁橋為研究對象，選取模態(tài)柔度差指標(biāo)作為遺傳優(yōu)化神經(jīng)網(wǎng)絡(luò)的輸入?yún)?shù)，驗證了方法在單位置及多位置損傷識別方面的有效性。結(jié)果表明：對于簡支梁橋，遺傳算法優(yōu)化神經(jīng)網(wǎng)絡(luò)對于單位置損傷識別的最大相對誤差為2.67%，對于多位置損傷識別的最大相對誤差為6.83%；對于連續(xù)梁橋，遺傳算法優(yōu)化神經(jīng)網(wǎng)絡(luò)對于單位置損傷識別的最大相對誤差為3.83%，對于多位置損傷識別的最大相對誤差為8.14%。 （4）以BP神經(jīng)網(wǎng)絡(luò)的對比分析表明，遺傳優(yōu)化神經(jīng)網(wǎng)絡(luò)對于簡支梁橋單位置損傷識別的最大誤差為2.67%，而BP神經(jīng)網(wǎng)絡(luò)的最大識別誤差為4%，遺傳優(yōu)化神經(jīng)網(wǎng)絡(luò)的計算精度要優(yōu)于BP網(wǎng)絡(luò)。
[Abstract]:Bridge structure, the most important form of traffic infrastructure, is widely used in road construction. The occurrence of bridge disasters and accidents results in huge casualties and economic losses. The occurrence of the disaster accident indicates that the bridge structure is easy to collapse if it is not detected, repaired or strengthened in time under the action of invisible internal damage. Timely and scientific damage detection and evaluation of bridge structures and timely understanding of structural damage conditions are of far-reaching significance to ensure the safe operation of structures and to avoid disasters and accidents. In this paper, the widely used simply supported beam bridge and continuous beam bridge are taken as the research objects, and the optimization work based on the damage identification index of the dynamic characteristics of the structure is carried out, and on this basis, the corresponding damage degree identification method is proposed. The main work is as follows:. 1) based on the damage identification theory of dynamic characteristics, the sensitivity of frequency, mode shape, modal curvature, modal flexibility and uniform load surface curvature to damage location identification is compared and analyzed. The results show that the modal curvature is poor. The index of modal flexibility difference curvature and uniform load surface curvature difference can realize effective damage location. (2) the anti-noise ability of modal flexibility difference curvature, modal curvature difference and uniform load surface curvature difference is analyzed. The results show that modal flexibility difference curvature has strong anti-noise acoustical interference ability and can be used as an optimal index for structural damage identification. The index is applied to the damage identification of continuous girder bridge. Taking simply supported beam bridge and continuous beam bridge as the research object, the modal flexibility index is selected as the input parameter of genetic optimization neural network. The effectiveness of the method in single position and multi-position damage identification is verified. The results show that: for simply supported beam bridges, The maximum relative error of genetic algorithm optimization neural network is 2.67 for single position damage identification, 6.83 for multi-position damage recognition, and 6.83 for continuous beam bridge. The maximum relative error of genetic algorithm for single position damage identification is 3.83 and 8.14 for multi-position damage identification. 4) the comparative analysis of BP neural network shows that, The maximum error of genetic optimization neural network for single position damage identification of simply supported beam bridge is 2.67 and the maximum recognition error of BP neural network is 4. The accuracy of genetic optimization neural network is better than that of BP neural network.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U445.7

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