基于光纤光栅传感的桥梁损伤识别与评估系统研究
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摘要
随着国民经济的发展,新的大型桥梁数目日益增多的同时,现役桥梁由于受气候、环境等因素的影响以及长期的静、动荷载作用,其强度和刚度随着时间的增加而降低,各种损伤也随之产生,给桥梁带来重大的安全隐患。因此对于桥梁的健康监测一直是桥梁专家们关注的课题。
桥梁的健康监测不只是传统的桥梁检测技术的简单改进,一个真正意义上的桥梁健康监测系统应该具有以下功能:由传感器监测环境荷载(风、地震、温度和交通荷载等)以及结构整体性态变量(如结构位移和加速度等)和局部性态变量(如应变等);能实现数据的同步采集、远程传输并对数据实施有效管理;能对数据进行分析处理,从而实现系统的参数识别、结构有限元模型修正以及结构的损伤识别和定位等功能;最后是损伤后处理,是基于对桥梁的损伤状态进行准确判定的情况下,对损伤桥梁的损伤状态进行定量评估,分析桥梁的健康状况,以及更进一步的对损伤桥梁的后处理提出解决方案,确定经济、科学的桥梁维修、养护策略。前两个功能是健康监测系统的基础,后两个功能是健康监测系统的核心和最终目标。
本文以光纤光栅振动传感器和振动数据采集系统的设计为基础,重点研究了健康监测系统的核心:损伤识别和损伤状态评估。根据神经网络和粗糙集这两种信息处理方法的各自特点,分别将其应用于桥梁的损伤识别和损伤后桥梁的损伤状态等级评估。提出了建立以光纤光栅振动传感器为数据基础的桥梁损伤评估系统的基本构想。为此,本文主要进行了以下几个方面的研究工作。
(1)为了获得准确有效的结构振动响应数据以作为正确的损伤识别的数据基础。设计了基于匹配光栅滤波解调的光纤光栅振动传感器,既解决了波长解调速度的问题,又消除了温度变化对传感器的影响,让灵敏度与频率响应这对矛盾达到很好的统一。数据采集通过USB口进行,在LabVIEW环境下调用采集卡的动态链接库函数进行多通道数据采集,实现数据的采集、显示和存储。为损伤识别提供有效的数据保障。
(2)基于频响函数的损伤识别法可以避免复杂的模态分析,并能在频域范围内提供更多的结构状态信息,采用主成分分析对频响函数进行有效的数据压缩和特征抽取,解决了基于频响函数的损伤识别在实际应用中面临的数据过多的问题。
(3)将自组织映射神经网络应用于桥梁的损伤模式识别,通过对钢箱梁模型模拟的各种常见损伤模式的测试,将频响函数的主成分作为输入向量的自组织映射神经网络,在经过足够的训练后,基本可以有效识别各损伤模式,实现对损伤的定位和定量分析。
(4)损伤后桥梁的状态等级评估是对桥梁进行管理和养护的基础。在前人工作的基础上,以《公路养护技术规范》为参考,对桥梁各子结构状态和整体损伤状态进行了离散分级,并利用粗糙集抽取桥梁各参数与整体损伤状态之间的分类规则,实现对于桥梁的损伤状态的分级评估,不仅对桥梁的维修和养护提供依据,同时粗糙集约简的结果也可以为桥梁的结构分析提供参考。
Along with the development of the national economy, more and more large-span bridges are constructed, and the existing bridges suffer from the weather, environment factors and the action of the static and active loads imposed on them. The strength and stiffness will degrade with the time running, and damages will occur as the result, which endanger the safety of the bridges. Accordingly, the bridge safety monitoring has been received more and more concern from structure experts.
A truly bridge safety monitoring system is not just the simple improve of the monitoring methods. It should be involved the functions as following: Monitoring the environment load and the entire and local state variable with various sensors; Acquire the response data of the bridge synchronously and manage the data effectively; Analyze the acquired data, process the system identification, structural infinite model modification and damage detection; the last but not least, is the after-damage management, quantificationally evaluate the damage state of the bridge according to the correct determinant of damage detection. The scientific repairment and maintenance methods are presented consequently. The last two functions are the core and the final goal of safety monitoring system.
Based on the fiber grating vibration sensor and vibration response acquisition system, the core technology of bridge safety monitoring system is discussed in this paper. Neural network and rough set algorithm are suggested to be used in bridge damage detection and damage state evaluation respectively. Therefore, the following research work has been carried out in this doctoral dissertation.
(1) As vibration testing has become the most effective method for the damage detection of bridge, an effective and dependable vibration testing system is developed. Fiber grating vibration sensor based on matched filtering demodulation is adopted to monitoring the vibration response data of the bridge. The problems of wave length demodulation speed and temperature error are solved effectively. The data acquisition system is developed based on USB. The software is carried out in LabVIEW by calling dynamic link library functions.
(2) The measured frequency response functions (FRF) is used as the input to artificial neural networks (ANN) because it can provide more structural information in frequency domain. Since full size of FRF data is too much for the ANN, a data reduction technique based on principal component analysis (PCA) is applied to extract the features. The extracted features are used as the input data of ANN instead of the raw FRF data.
(3) The self-organizing map neural network is chosen because of its superiority in analyzing high-dimensional data without supervising. A steel box girder model with multi damage states is presented to demonstrate the effectiveness of the method.
(4) Damage state evaluation of the damaged bridges can provide scientific reference for the bridge management and maintance. Take the advantage in processing incomplete information without any preliminary or additional information about data, rough set theory is used to mine rules between the discretized testing data of each branch construction and the state of the bridge. The evaluating results can offer valuable perspectives of structural design and analysis as well.
桥梁的健康监测不只是传统的桥梁检测技术的简单改进,一个真正意义上的桥梁健康监测系统应该具有以下功能:由传感器监测环境荷载(风、地震、温度和交通荷载等)以及结构整体性态变量(如结构位移和加速度等)和局部性态变量(如应变等);能实现数据的同步采集、远程传输并对数据实施有效管理;能对数据进行分析处理,从而实现系统的参数识别、结构有限元模型修正以及结构的损伤识别和定位等功能;最后是损伤后处理,是基于对桥梁的损伤状态进行准确判定的情况下,对损伤桥梁的损伤状态进行定量评估,分析桥梁的健康状况,以及更进一步的对损伤桥梁的后处理提出解决方案,确定经济、科学的桥梁维修、养护策略。前两个功能是健康监测系统的基础,后两个功能是健康监测系统的核心和最终目标。
本文以光纤光栅振动传感器和振动数据采集系统的设计为基础,重点研究了健康监测系统的核心:损伤识别和损伤状态评估。根据神经网络和粗糙集这两种信息处理方法的各自特点,分别将其应用于桥梁的损伤识别和损伤后桥梁的损伤状态等级评估。提出了建立以光纤光栅振动传感器为数据基础的桥梁损伤评估系统的基本构想。为此,本文主要进行了以下几个方面的研究工作。
(1)为了获得准确有效的结构振动响应数据以作为正确的损伤识别的数据基础。设计了基于匹配光栅滤波解调的光纤光栅振动传感器,既解决了波长解调速度的问题,又消除了温度变化对传感器的影响,让灵敏度与频率响应这对矛盾达到很好的统一。数据采集通过USB口进行,在LabVIEW环境下调用采集卡的动态链接库函数进行多通道数据采集,实现数据的采集、显示和存储。为损伤识别提供有效的数据保障。
(2)基于频响函数的损伤识别法可以避免复杂的模态分析,并能在频域范围内提供更多的结构状态信息,采用主成分分析对频响函数进行有效的数据压缩和特征抽取,解决了基于频响函数的损伤识别在实际应用中面临的数据过多的问题。
(3)将自组织映射神经网络应用于桥梁的损伤模式识别,通过对钢箱梁模型模拟的各种常见损伤模式的测试,将频响函数的主成分作为输入向量的自组织映射神经网络,在经过足够的训练后,基本可以有效识别各损伤模式,实现对损伤的定位和定量分析。
(4)损伤后桥梁的状态等级评估是对桥梁进行管理和养护的基础。在前人工作的基础上,以《公路养护技术规范》为参考,对桥梁各子结构状态和整体损伤状态进行了离散分级,并利用粗糙集抽取桥梁各参数与整体损伤状态之间的分类规则,实现对于桥梁的损伤状态的分级评估,不仅对桥梁的维修和养护提供依据,同时粗糙集约简的结果也可以为桥梁的结构分析提供参考。
Along with the development of the national economy, more and more large-span bridges are constructed, and the existing bridges suffer from the weather, environment factors and the action of the static and active loads imposed on them. The strength and stiffness will degrade with the time running, and damages will occur as the result, which endanger the safety of the bridges. Accordingly, the bridge safety monitoring has been received more and more concern from structure experts.
A truly bridge safety monitoring system is not just the simple improve of the monitoring methods. It should be involved the functions as following: Monitoring the environment load and the entire and local state variable with various sensors; Acquire the response data of the bridge synchronously and manage the data effectively; Analyze the acquired data, process the system identification, structural infinite model modification and damage detection; the last but not least, is the after-damage management, quantificationally evaluate the damage state of the bridge according to the correct determinant of damage detection. The scientific repairment and maintenance methods are presented consequently. The last two functions are the core and the final goal of safety monitoring system.
Based on the fiber grating vibration sensor and vibration response acquisition system, the core technology of bridge safety monitoring system is discussed in this paper. Neural network and rough set algorithm are suggested to be used in bridge damage detection and damage state evaluation respectively. Therefore, the following research work has been carried out in this doctoral dissertation.
(1) As vibration testing has become the most effective method for the damage detection of bridge, an effective and dependable vibration testing system is developed. Fiber grating vibration sensor based on matched filtering demodulation is adopted to monitoring the vibration response data of the bridge. The problems of wave length demodulation speed and temperature error are solved effectively. The data acquisition system is developed based on USB. The software is carried out in LabVIEW by calling dynamic link library functions.
(2) The measured frequency response functions (FRF) is used as the input to artificial neural networks (ANN) because it can provide more structural information in frequency domain. Since full size of FRF data is too much for the ANN, a data reduction technique based on principal component analysis (PCA) is applied to extract the features. The extracted features are used as the input data of ANN instead of the raw FRF data.
(3) The self-organizing map neural network is chosen because of its superiority in analyzing high-dimensional data without supervising. A steel box girder model with multi damage states is presented to demonstrate the effectiveness of the method.
(4) Damage state evaluation of the damaged bridges can provide scientific reference for the bridge management and maintance. Take the advantage in processing incomplete information without any preliminary or additional information about data, rough set theory is used to mine rules between the discretized testing data of each branch construction and the state of the bridge. The evaluating results can offer valuable perspectives of structural design and analysis as well.
引文
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