大跨混合梁斜拉桥健康监测系统及智能诊断方法研究
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摘要
随着我国交通事业的快速发展,大型、新颖的桥梁结构形式不断涌现,新旧桥梁数量日益增多。为确保人民生命财产安全,快速有效地识别出桥梁结构可能发生损伤的部位和损伤程度,及时掌握桥梁运营状态下的健康状况,是当前桥梁工程研究领域的热点问题之一。
本文以天津市中心城区快速路工程河北大街立交工程主桥混合梁斜拉桥为工程背景,结合该桥的结构特点,建立其健康监测系统;并重点针对整个健康监测系统中的健康诊断子系统进行了研究。在研究过程中,根据分步识别理论,将健康诊断过程分为两个阶段:子结构的损伤识别和子结构构件的损伤识别。提出了联合采用概率神经网络和径向基函数神经网络进行子结构分阶段损伤识别的智能诊断方法,并模拟全桥的损伤识别过程。其中,第一阶段采用概率神经网络进行子结构的损伤识别;第二阶段采用径向基函数神经网络进行钢主梁子结构局部构件的损伤识别,在识别过程中,结合该桥的结构特点及所建立的健康监测系统可获取的结构响应参数(动力响应参数和静力响应参数),提出了动-静组合损伤指标,并建立相应的径向基函数网络模型,分别针对单损伤、双损伤和三损伤的不同损伤情况进行模拟。
研究结果表明,联合采用概率神经网络和径向基函数神经网络的智能诊断方法效率较高,可满足健康诊断子系统对损伤识别的要求。同时,所提出的动-静组合损伤指标对混合梁斜拉桥的损伤比较敏感,可以应用于大型混合梁桥结构的损伤识别过程中。为建立大型复杂桥梁结构的健康诊断系统提供一定的参考。
With the expeditious development of our civil traffic projects, plenty of oversize and novel bridges have constantly rushed, the number of new and old bridges has been increased. To keep our people in a safe condition upon their lives and treasure, immediate and effective detection towards the positions and extent of damage on bridges and get an immediate idea of the its’health condition in daily use has been brought as a problem into the researchers’focus.
The Hebei Street cable-stayed hybird girder bridge in Tianjin was investigated in the dissertation. Take into account of its structural characteristic, the bridge structural health monitoring systerm is constituded and a lot of efforts have been put on the study of the subsystem of health diagnosis. According to the theory of hierarchical damage identification, the course of health diagnosis is supposed to be divided into two steps, i.e. the substructural damage identification and the substructural components damage identification. The damage identification of the bridge in whole span is simulated, and an intelligent diagnosis method for damage identification is proposed in substructural hierarchical damage identification by using the combination of the Probablistic Neural Network (PNN) and the Radial Basis Function (RBF) Neural Network. The first step is to detect the damaged substructure using the PNN, and the second step is to detect the damaged substructural components with the RBF Neural Network. Especially in the second step, an assembled static and dynamic damage sensitive index is presented according to the structural characteristic and the structural response indices including the available dynamic and static response indices. Then, a model of RBF Neural Network is constituted and is used to simulate three damage conditions, i.e. single damage and double or three damages which occured simultaneously.
The results indicate that the intelligent diagnosis method combining the PNN and the RBF Neural Network can meet the requirement of health diagnosis subsystem for damage identification with great efficiency. The assembled static and dynamic index shows a sensitive performance during identification of the cable-stayed hybird girder bridge, and it is suitable for the process of damage diagnosis of long-span hybird girder bridge structure. In short, this research could give some useful references towards the constitution of health diagnoses system of long-span complex bridges.
本文以天津市中心城区快速路工程河北大街立交工程主桥混合梁斜拉桥为工程背景,结合该桥的结构特点,建立其健康监测系统;并重点针对整个健康监测系统中的健康诊断子系统进行了研究。在研究过程中,根据分步识别理论,将健康诊断过程分为两个阶段:子结构的损伤识别和子结构构件的损伤识别。提出了联合采用概率神经网络和径向基函数神经网络进行子结构分阶段损伤识别的智能诊断方法,并模拟全桥的损伤识别过程。其中,第一阶段采用概率神经网络进行子结构的损伤识别;第二阶段采用径向基函数神经网络进行钢主梁子结构局部构件的损伤识别,在识别过程中,结合该桥的结构特点及所建立的健康监测系统可获取的结构响应参数(动力响应参数和静力响应参数),提出了动-静组合损伤指标,并建立相应的径向基函数网络模型,分别针对单损伤、双损伤和三损伤的不同损伤情况进行模拟。
研究结果表明,联合采用概率神经网络和径向基函数神经网络的智能诊断方法效率较高,可满足健康诊断子系统对损伤识别的要求。同时,所提出的动-静组合损伤指标对混合梁斜拉桥的损伤比较敏感,可以应用于大型混合梁桥结构的损伤识别过程中。为建立大型复杂桥梁结构的健康诊断系统提供一定的参考。
With the expeditious development of our civil traffic projects, plenty of oversize and novel bridges have constantly rushed, the number of new and old bridges has been increased. To keep our people in a safe condition upon their lives and treasure, immediate and effective detection towards the positions and extent of damage on bridges and get an immediate idea of the its’health condition in daily use has been brought as a problem into the researchers’focus.
The Hebei Street cable-stayed hybird girder bridge in Tianjin was investigated in the dissertation. Take into account of its structural characteristic, the bridge structural health monitoring systerm is constituded and a lot of efforts have been put on the study of the subsystem of health diagnosis. According to the theory of hierarchical damage identification, the course of health diagnosis is supposed to be divided into two steps, i.e. the substructural damage identification and the substructural components damage identification. The damage identification of the bridge in whole span is simulated, and an intelligent diagnosis method for damage identification is proposed in substructural hierarchical damage identification by using the combination of the Probablistic Neural Network (PNN) and the Radial Basis Function (RBF) Neural Network. The first step is to detect the damaged substructure using the PNN, and the second step is to detect the damaged substructural components with the RBF Neural Network. Especially in the second step, an assembled static and dynamic damage sensitive index is presented according to the structural characteristic and the structural response indices including the available dynamic and static response indices. Then, a model of RBF Neural Network is constituted and is used to simulate three damage conditions, i.e. single damage and double or three damages which occured simultaneously.
The results indicate that the intelligent diagnosis method combining the PNN and the RBF Neural Network can meet the requirement of health diagnosis subsystem for damage identification with great efficiency. The assembled static and dynamic index shows a sensitive performance during identification of the cable-stayed hybird girder bridge, and it is suitable for the process of damage diagnosis of long-span hybird girder bridge structure. In short, this research could give some useful references towards the constitution of health diagnoses system of long-span complex bridges.
引文
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[20]邓焱,严普强.桥梁应变模态与损伤测量的新方法[J],清华大学学报(自然科学版),2000,40(1):123~127
[21]禹丹江,陈淮.桥梁损伤检测的曲率模态方法探讨[J],郑州大学学报(工学版),2002,23(3):104~106
[22]李德葆,陆秋海等.承弯结构的曲率模态分析[J],清华大学学报(自然科学版),2002,42(2):224~227
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