【摘要】：隨著橋梁結(jié)構(gòu)規(guī)模越來(lái)越大,受力特性趨于復(fù)雜,建立橋梁健康監(jiān)測(cè)系統(tǒng)能最大限度監(jiān)控其工作狀態(tài)。其中加速度傳感器測(cè)點(diǎn)的優(yōu)化布置直接關(guān)系著監(jiān)測(cè)系統(tǒng)信息的采集效果,因此需要用最有限的測(cè)點(diǎn)滿(mǎn)足橋梁健康監(jiān)測(cè)的需求。當(dāng)確定出加速度傳感器測(cè)點(diǎn)的優(yōu)化布置方案后,運(yùn)行橋梁健康監(jiān)測(cè)系統(tǒng),就涉及到對(duì)相關(guān)測(cè)點(diǎn)采集的加速度信號(hào)進(jìn)行分析處理。本文結(jié)合數(shù)值模擬和工程案例,對(duì)幾種不同的測(cè)點(diǎn)優(yōu)化布置算法進(jìn)行了研究,并對(duì)實(shí)橋跨中豎向和橫向測(cè)點(diǎn)采集到的加速度信號(hào)采用3種不同的信號(hào)分析處理方法進(jìn)行分析,論文主要研究有:(1)對(duì)橋梁結(jié)構(gòu)的健康監(jiān)測(cè)研究現(xiàn)狀進(jìn)行了總結(jié),同時(shí)構(gòu)建出傳感器優(yōu)化布置的數(shù)學(xué)模型,最后針對(duì)目前常用的3種信號(hào)分析處理方法進(jìn)行了總結(jié)。針對(duì)傳感器優(yōu)化布置的方法:有效獨(dú)立算法、遺傳算法、模擬退火算法和蟻群算法原理進(jìn)行了詳細(xì)研究;對(duì)信號(hào)分析的方法:快速傅里葉變換、小波變換和HHT變換進(jìn)行了詳細(xì)研究。(2)詳細(xì)分析了軟計(jì)算(遺傳算法、模擬退火算法和蟻群算法)的收斂性,編寫(xiě)出軟計(jì)算的MATLAB程序。以4個(gè)典型函數(shù)為數(shù)值實(shí)驗(yàn)平臺(tái),分別采用這3種算法進(jìn)行對(duì)比實(shí)驗(yàn)分析,分析其優(yōu)化性能和適用范圍。以實(shí)際比較復(fù)雜的測(cè)點(diǎn)最優(yōu)路徑優(yōu)化應(yīng)用問(wèn)題,檢驗(yàn)軟計(jì)算的實(shí)際應(yīng)用性能。當(dāng)只有10個(gè)測(cè)點(diǎn)時(shí),3種算法均能快速收斂到全局最優(yōu)解,但當(dāng)有100個(gè)測(cè)點(diǎn)時(shí),只能求得近似解。(3)針對(duì)主跨1092m的滬通長(zhǎng)江大橋,采用MIDAS CIVIL2013建立有限元模型,然后進(jìn)行模態(tài)分析,提取相關(guān)模態(tài)信息,然后分別采用有效獨(dú)立法、改進(jìn)遺傳算法和模擬退火算法對(duì)主桁架豎向加速度測(cè)點(diǎn)和橋塔縱向傾角測(cè)點(diǎn)進(jìn)行優(yōu)化布置。根據(jù)優(yōu)化結(jié)果,對(duì)比研究不同算法的優(yōu)化效果和特點(diǎn),確定綜合布置方案。(4)根據(jù)滬通長(zhǎng)江大橋的主桁架豎向加速度傳感器優(yōu)化布置方案,在滬通長(zhǎng)江大橋的MIDAS有限元模型中,給橋梁結(jié)構(gòu)分別施加單位脈沖荷載和白噪聲激勵(lì),提取出傳感器測(cè)點(diǎn)位置的加速度響應(yīng)信號(hào),進(jìn)行信號(hào)的時(shí)域分析,識(shí)別橋梁的模態(tài)參數(shù)。(5)針對(duì)某大橋跨中豎向和橫向加速度傳感器測(cè)點(diǎn)在車(chē)輛荷載激勵(lì)和白噪聲環(huán)境激勵(lì)下采集的非平穩(wěn)加速度信號(hào),主要采用HHT變換進(jìn)行分析,并通過(guò)對(duì)比HHT變換與FFT變換和小波變換的分析結(jié)果,證明了對(duì)于健康監(jiān)測(cè)系統(tǒng)中采集的非平穩(wěn)信號(hào),采用HHT變換進(jìn)行分析能得到比較精確的結(jié)果,Hilbert譜刻畫(huà)的頻率-時(shí)間-振幅圖有很高的分辨率,同時(shí)能有效快速識(shí)別出原始信號(hào)中的不同頻譜帶。
[Abstract]:With the increasing scale of the bridge structure and the complexity of the mechanical characteristics, the establishment of the bridge health monitoring system can monitor its working state to the maximum extent. The optimal layout of measuring points of acceleration sensor is directly related to the information collection effect of monitoring system, so it is necessary to use the most limited measuring points to meet the needs of bridge health monitoring. When the optimal layout of the measuring points of the acceleration sensor is determined, the bridge health monitoring system is operated, which involves the analysis and processing of the acceleration signals collected from the relevant measuring points. Combined with numerical simulation and engineering cases, this paper studies several different algorithms for optimal layout of measuring points, and analyzes the acceleration signals collected from vertical and lateral measuring points in real bridge span by three different signal analysis and processing methods. The main research contents are as follows: (1) the status quo of bridge structure health monitoring is summarized, and the mathematical model of sensor optimal arrangement is constructed. Finally, three commonly used signal analysis and processing methods are summarized. The principles of effective independent algorithm, genetic algorithm, simulated annealing algorithm and ant colony algorithm are studied in detail. The methods of signal analysis: fast Fourier transform, wavelet transform and HHT transform are studied in detail. (2) the convergence of soft computing (genetic algorithm, simulated annealing algorithm and ant colony algorithm) is analyzed in detail. Write a soft computing MATLAB program. Taking four typical functions as the numerical experimental platform, the three algorithms are used to compare and analyze the optimization performance and the applicable range. The practical application performance of soft computing is tested by using the practical application problem of the optimal path optimization of the measurement point. When there are only 10 measuring points, the three algorithms can quickly converge to the global optimal solution, but when there are 100 measuring points, the approximate solution can only be obtained. (3) for the main span 1092m, the finite element model of the Hutong Yangtze River Bridge is established by using MIDAS CIVIL2013. Then modal analysis was carried out to extract the relevant modal information. Then the effective independent method was used to optimize the vertical acceleration measurement points of the main truss and the longitudinal obliquity of the bridge tower by using genetic algorithm and simulated annealing algorithm respectively. According to the optimization results, the optimization effects and characteristics of different algorithms are compared and the comprehensive layout scheme is determined. (4) according to the optimization scheme of vertical acceleration sensors of the main truss of the Hutong Yangtze River Bridge, In the MIDAS finite element model of the Hutong Yangtze River Bridge, the acceleration response signal of the sensor point position is extracted by applying unit pulse load and white noise excitation to the bridge structure, and the time domain analysis of the signal is carried out. Identification of modal parameters of bridge. (5) the non-stationary acceleration signals collected by vertical and transverse acceleration sensor points in a bridge span under vehicle load excitation and white noise environment excitation are mainly analyzed by HHT transform. By comparing the analysis results of HHT transform with FFT transform and wavelet transform, it is proved that the non-stationary signal collected in the health monitoring system can be analyzed with HHT transform. The frequency-time-amplitude pattern characterized by Hilbert spectrum has high resolution and can identify the different spectrum bands in the original signal efficiently and quickly.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U446

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