大跨度钢—混凝土组合铁路桥梁健康诊断分析研究

发布时间：2018-01-15 05:18

本文关键词：大跨度钢—混凝土组合铁路桥梁健康诊断分析研究　出处：《兰州交通大学》2014年硕士论文　论文类型：学位论文

【摘要】：在环境因素和超载等荷载的作用下，桥梁结构会产生老化现象，或出现损伤。如果任由结构的损伤发展，不仅会使桥梁承载力降低，严重的时候还会使整个桥梁结构破坏。近年来，我国桥梁事故频出，，造成了严重的财产损伤，甚至是生命的损失。因此，对桥梁结构进行健康诊断和及时的了解桥梁的损伤情况，这是十分有意义的，也是非常必要的。本文以西平铁路太峪80m钢-混凝土组合桁梁桥为工程背景。对该桥梁的结构特点，损伤易产生的部位，产生损伤的典型原因进行了分析研究。对该桥梁在损伤工况下的动力特性和静力学特性进行计算，并与该桥梁在无损状态下的计算结果进行对比分析，得出损伤对该类型桥梁的影响特点。使用有限元软件Midas/Civil进行建模，并模拟各种损伤工况，提取相应的损伤指标数据。结合MATLAB软件，完成RBF神经网络的构建、学习和训练，并使用训练好的神经网络进行桥梁健康诊断。主要研究内容如下： (1)收集了国内外对于桥梁健康诊断的各种方法的资料，分析总结各种健康诊断方法的优劣性和适用范围，找出钢-混凝土组合桁梁适用的健康诊断方法。 (2)在掌握神经网络理论的基础上，结合桥梁损伤诊断和神经网络的特点，设计桥梁损伤诊断的RBF神经网络，确定RBF神经网络损伤诊断的步骤。并通过Matlab（2013b版本）软件使RBF神经网络健康诊断方法得到实现。 (3)研究钢-混凝土组合桁梁桥的结构特点，分析判断该桥梁的易损伤部位，并对典型的损伤因素进行分析研究。在不同损伤工况下计算该桥梁的静力学特性和动力特性，并与无损状态的计算结果进行了对比，得出损伤对该类型桥梁的影响特点。 (4)针对钢-混凝土组合桁梁桥的特点，找出能够有效反应该桥梁损伤的损伤指标。构建RBF神经网络，提取有效的训练样本对RBF神经网络进行训练。使用训练好的神经网络进行钢-混凝土组合桁梁桥的损伤诊断，并判定诊断结果的有效性。 (5)使用RBF神经网络健康诊断方法，以结构自振频率为损伤指标，可以有效的对桥梁结构进行异常检测；以振型模态为损伤指标，可以有效的对钢-混凝土组合桁梁的单腹杆损伤、单节点损伤、腹杆和节点混合损伤进行损伤程度和损伤位置的判定。RBF神经网络健康诊断方法对于钢-混凝土组合桁梁的健康诊断是适用有效的。
[Abstract]:Under the action of environmental factors and overload, the bridge structure will produce aging phenomenon or damage. If the damage of the structure is allowed to develop, it will not only reduce the bearing capacity of the bridge. In recent years, bridge accidents occur frequently in our country, resulting in serious property damage, even loss of life. It is very meaningful and necessary to make a healthy diagnosis of the bridge structure and understand the damage of the bridge in time. This paper takes Taiyu 80m steel and concrete composite truss bridge of Xiping Railway as the engineering background. The typical causes of the damage are analyzed and studied. The dynamic and static characteristics of the bridge under the damaged condition are calculated and compared with the results of the bridge in the non-destructive state. The effects of damage on this type of bridge are obtained. The finite element software Midas/Civil is used to model and simulate various damage conditions. The corresponding damage index data are extracted, and the RBF neural network is constructed, studied and trained with MATLAB software. The trained neural network is used for bridge health diagnosis. The main contents of the study are as follows: 1) collecting the data of various methods of bridge health diagnosis at home and abroad, analyzing and summarizing the advantages and disadvantages and applicable scope of various health diagnosis methods. The health diagnosis method for steel-concrete composite truss beam is found out. Based on the theory of neural network and the characteristics of bridge damage diagnosis and neural network, the RBF neural network for bridge damage diagnosis is designed. The steps of RBF neural network damage diagnosis are determined, and the method of RBF neural network health diagnosis is realized by Matlab(2013b software. 3) the structural characteristics of steel-concrete composite truss bridge are studied, and the vulnerable parts of the bridge are analyzed and judged. The static and dynamic characteristics of the bridge are calculated under different damage conditions, and the results are compared with those of the non-destructive state. The effects of damage on this type of bridge are obtained. 4) according to the characteristics of steel-concrete composite truss bridge, the damage index which can effectively reflect the damage of the bridge is found, and the RBF neural network is constructed. The RBF neural network is trained with effective training samples, and the damage diagnosis of steel-concrete composite truss bridge is carried out by using the trained neural network, and the validity of the diagnosis result is determined. 5) using the RBF neural network health diagnosis method, taking the natural vibration frequency of the structure as the damage index, the bridge structure can be effectively detected. Taking the mode mode as the damage index, it can effectively damage the single web bar and single node of the steel-concrete composite truss. The method of RBF neural network health diagnosis is applicable to the health diagnosis of steel-concrete composite truss beam.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U446

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