基于改进遗传算法的桥梁监测传感器测点优化布置研究及监测信号处理
发布时间：2018-11-06 18:47
【摘要】：随着桥梁结构规模越来越大,受力特性趋于复杂,建立桥梁健康监测系统能最大限度监控其工作状态。其中加速度传感器测点的优化布置直接关系着监测系统信息的采集效果,因此需要用最有限的测点满足桥梁健康监测的需求。当确定出加速度传感器测点的优化布置方案后,运行桥梁健康监测系统,就涉及到对相关测点采集的加速度信号进行分析处理。本文结合数值模拟和工程案例,对几种不同的测点优化布置算法进行了研究,并对实桥跨中竖向和横向测点采集到的加速度信号采用3种不同的信号分析处理方法进行分析,论文主要研究有:(1)对桥梁结构的健康监测研究现状进行了总结,同时构建出传感器优化布置的数学模型,最后针对目前常用的3种信号分析处理方法进行了总结。针对传感器优化布置的方法:有效独立算法、遗传算法、模拟退火算法和蚁群算法原理进行了详细研究;对信号分析的方法:快速傅里叶变换、小波变换和HHT变换进行了详细研究。(2)详细分析了软计算(遗传算法、模拟退火算法和蚁群算法)的收敛性,编写出软计算的MATLAB程序。以4个典型函数为数值实验平台,分别采用这3种算法进行对比实验分析,分析其优化性能和适用范围。以实际比较复杂的测点最优路径优化应用问题,检验软计算的实际应用性能。当只有10个测点时,3种算法均能快速收敛到全局最优解,但当有100个测点时,只能求得近似解。(3)针对主跨1092m的沪通长江大桥,采用MIDAS CIVIL2013建立有限元模型,然后进行模态分析,提取相关模态信息,然后分别采用有效独立法、改进遗传算法和模拟退火算法对主桁架竖向加速度测点和桥塔纵向倾角测点进行优化布置。根据优化结果,对比研究不同算法的优化效果和特点,确定综合布置方案。(4)根据沪通长江大桥的主桁架竖向加速度传感器优化布置方案,在沪通长江大桥的MIDAS有限元模型中,给桥梁结构分别施加单位脉冲荷载和白噪声激励,提取出传感器测点位置的加速度响应信号,进行信号的时域分析,识别桥梁的模态参数。(5)针对某大桥跨中竖向和横向加速度传感器测点在车辆荷载激励和白噪声环境激励下采集的非平稳加速度信号,主要采用HHT变换进行分析,并通过对比HHT变换与FFT变换和小波变换的分析结果,证明了对于健康监测系统中采集的非平稳信号,采用HHT变换进行分析能得到比较精确的结果,Hilbert谱刻画的频率-时间-振幅图有很高的分辨率,同时能有效快速识别出原始信号中的不同频谱带。
[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.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U446
本文编号：2315149
[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.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U446
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