基于神经网络的空间网架有限元模型修正
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摘要
现代的许多土木结构正不断向大型化、复杂化方向发展，新型结构也不断出现，同时，我国在役结构物中有许多建筑物存在不同程度损伤或结构已经进入服役后期，急需损伤鉴定与维修加固，因此结构损伤诊断方法的研究成为新世纪土木工程领域的一个前沿研究热点。
对建筑结构进行动力响应预测、振动控制和结构状态评估及健康监测，首先必需详细了解结构的动力特性。结构的动力特性和结构参数直接相关，这些模态特性可以通过有限元分析得到其理论值，也可以由实验模态分析得到其实测值。由于结构的损伤将引起相应动力特性的改变，结构自振频率的实测值与有限元理论值之间常常存在较大的差异，则需要解决的问题是如何修正结构的有限元模型，使得结构模态特性的理论值趋近于实测值。神经网络方法因其具有非线性映射能力强、计算速度快、容错性好等优点，非常适合于结构有限元模型修正。但实验数据不完备、结构损伤标识量的选取、神经网络的选定等问题没有得到很好的解决。
本文的工程背景为深圳市民中心屋顶网架的健康监测。以工程中广泛应用的网架结构为对象，针对其节点固结系数，利用结构的频率与模态变化量为损伤标识量，研究了神经网络方法在结构有限元模型修正中的应用。
本文建立了节点连接刚度发生变化的网架结构的三维有限元模型，定义了固结系数来描述节点的单元刚度变化，研究了用于振动测试的加速度传感器的优化配置与网架结构模态参数的实测方法。
本文提出了基于神经网络对网架结构有限元模型进行修正的方法。首先确定网架结构以固结系数表示的三维空间有限元模型，根据单元刚度矩阵中的固结系数的变化，建立固结系数与结构动力特性的关系。其次，根据损伤后的结构频率与模态的变化，应用径向基神经网络，进行结构节点
固结系数的识别，从而实现对网架结构有限元模型的修正。
根据本文的研究可以得知，径向基神经网络可以很好的用于结构有限
元模型的修正，利用频率与模态作为损伤标识量，改善单纯频率作为损伤
标识量对结构损伤不敏感的特点，完全可以实现基于神经网络的对网架结
构的有限元模型修正。
Since more and more structures are becoming larger and more complex than before, there are many new structures in modern time. At the same time, there are various damage in many existed structures, and they have been used for several decades and are to be surveyed and strengthened. Therefore, structural damage diagnosis has become one of the advancing fronts of civil engineering researches.
To forecast structural response, vibration controk state evaluation and health monitor, the first thing is to know the structural dynamic characteristic. Structural dynamic characteristic is related to structural parameter, its theory value can be gained by finite element model (FEM) analysis, its practice value can be achieved by experiment modal analysis. Since the structural dynamic characteristic is changed for its damage, the large difference of structural frequency is existed between the theory value and the practice value. The problem that is to be resolved is how to correct structural FEM to make the theory value equal to the practice value, neural network technique is adapted to the FEM correction for its strong non-linear mapping ability, rapid computation and anti-interference capability. But there are still some problems to be solved such as selection of neural network, determination of structural damage indicator and incompletion of measurement.
The project background of this paper is the health monitor of roof truss of Shenzhen citizen center. The object is truss structures that are extensively used in civil engineering, the fixity factor is considered and the damage indicator of frequency and modal are used, and neural network technique that is used for FEM correction is studied in this paper.
The space FEM is established when joint link stiff is changed in this paper, the fixity factor is defined to show the element stiff change of joint, the
placement of sensor in an optimal fashion for vibration experiment and modal parameter identification of truss structure are studied.
The FEM correction method of truss structure based neural network technique is developed in this paper. Firstly, the space FEM of truss structure that based fixity factor is determined, the relation between fixity factor and structural dynamic characteristic is established according to the change of fixity factor of element stiff matrix. Secondly, according to the change of damage structural frequency and modal, the fixity factor is identified by RBF neural network, then FEM correction of truss is finished.
According to the research of this paper, RBF neural net can be well adapted to structure FEM correction, the damage indicator of frequency and modal is used to improve the characteristic that damage indicator of pure frequency is not sensitive to structural damage, the FEM correction of truss structure based neural network can be fully finished.
对建筑结构进行动力响应预测、振动控制和结构状态评估及健康监测，首先必需详细了解结构的动力特性。结构的动力特性和结构参数直接相关，这些模态特性可以通过有限元分析得到其理论值，也可以由实验模态分析得到其实测值。由于结构的损伤将引起相应动力特性的改变，结构自振频率的实测值与有限元理论值之间常常存在较大的差异，则需要解决的问题是如何修正结构的有限元模型，使得结构模态特性的理论值趋近于实测值。神经网络方法因其具有非线性映射能力强、计算速度快、容错性好等优点，非常适合于结构有限元模型修正。但实验数据不完备、结构损伤标识量的选取、神经网络的选定等问题没有得到很好的解决。
本文的工程背景为深圳市民中心屋顶网架的健康监测。以工程中广泛应用的网架结构为对象，针对其节点固结系数，利用结构的频率与模态变化量为损伤标识量，研究了神经网络方法在结构有限元模型修正中的应用。
本文建立了节点连接刚度发生变化的网架结构的三维有限元模型，定义了固结系数来描述节点的单元刚度变化，研究了用于振动测试的加速度传感器的优化配置与网架结构模态参数的实测方法。
本文提出了基于神经网络对网架结构有限元模型进行修正的方法。首先确定网架结构以固结系数表示的三维空间有限元模型，根据单元刚度矩阵中的固结系数的变化，建立固结系数与结构动力特性的关系。其次，根据损伤后的结构频率与模态的变化，应用径向基神经网络，进行结构节点
固结系数的识别，从而实现对网架结构有限元模型的修正。
根据本文的研究可以得知，径向基神经网络可以很好的用于结构有限
元模型的修正，利用频率与模态作为损伤标识量，改善单纯频率作为损伤
标识量对结构损伤不敏感的特点，完全可以实现基于神经网络的对网架结
构的有限元模型修正。
Since more and more structures are becoming larger and more complex than before, there are many new structures in modern time. At the same time, there are various damage in many existed structures, and they have been used for several decades and are to be surveyed and strengthened. Therefore, structural damage diagnosis has become one of the advancing fronts of civil engineering researches.
To forecast structural response, vibration controk state evaluation and health monitor, the first thing is to know the structural dynamic characteristic. Structural dynamic characteristic is related to structural parameter, its theory value can be gained by finite element model (FEM) analysis, its practice value can be achieved by experiment modal analysis. Since the structural dynamic characteristic is changed for its damage, the large difference of structural frequency is existed between the theory value and the practice value. The problem that is to be resolved is how to correct structural FEM to make the theory value equal to the practice value, neural network technique is adapted to the FEM correction for its strong non-linear mapping ability, rapid computation and anti-interference capability. But there are still some problems to be solved such as selection of neural network, determination of structural damage indicator and incompletion of measurement.
The project background of this paper is the health monitor of roof truss of Shenzhen citizen center. The object is truss structures that are extensively used in civil engineering, the fixity factor is considered and the damage indicator of frequency and modal are used, and neural network technique that is used for FEM correction is studied in this paper.
The space FEM is established when joint link stiff is changed in this paper, the fixity factor is defined to show the element stiff change of joint, the
placement of sensor in an optimal fashion for vibration experiment and modal parameter identification of truss structure are studied.
The FEM correction method of truss structure based neural network technique is developed in this paper. Firstly, the space FEM of truss structure that based fixity factor is determined, the relation between fixity factor and structural dynamic characteristic is established according to the change of fixity factor of element stiff matrix. Secondly, according to the change of damage structural frequency and modal, the fixity factor is identified by RBF neural network, then FEM correction of truss is finished.
According to the research of this paper, RBF neural net can be well adapted to structure FEM correction, the damage indicator of frequency and modal is used to improve the characteristic that damage indicator of pure frequency is not sensitive to structural damage, the FEM correction of truss structure based neural network can be fully finished.
引文
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4．苏娟、陆秋海、管迪华，神经网络法在定量损伤识别研究中的应用，清华大学学报，1999
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6．陆秋海、李德葆、张维，利用模态实验参数识别结构损伤的神经网络法，工程力学，1999
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9．易伟建、刘霞，结构损伤诊断的遗传算法研究，系统工程理论与实践，2001
10．王柏生、丁皓江，用于结构损伤识别的神经网络设计，工程设计，1999
11．于开平、邹经湘，模态参数识别的小波变换方法，宇航学报，1999
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13．冯文贤、陈新，基于实验复模态参数的有限元模型修正，航空学报，1999
14．刘福强、张令弥，一种改进的特征系统实现算法及在智能结构中的应用，振动工程，1999
15．秦权，桥梁结构的健康监测，中国公路学报，2000
16．李戈、秦权，董聪，用遗传算法选择悬索桥监测系统中传感器的最优布点，工程力学，2000
17．邓焱、严普强，桥梁结构损伤的振动模态检测，振动、测试与诊断，1999
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19．张启伟、袁万城、范立础，桥梁结构基于理论模型修正的损伤检测，1999
20．崔飞、袁万城、史家钧，传感器优化布设在桥梁健康监测中的应用，同济大学学报，1999
21．朱乐东，桥梁固有模态的识别，同济大学学报，1999
22．朱乐东、张之勇、张志成、朱宏平，香港汲水门大桥的模态识别，振动工程学报，1999
23．袁万城、崔飞、张启伟，桥梁健康监测与状态评估的研究现状与发展，同济大学学报，1999
24．范立础、袁万城、张启伟，悬索桥结构基于敏感性分析的动力有限元模型修正，土木工程学报，2000
25．李德葆，结构动力分析的应变模态法，机械强度，1990
26．蔡国平、赵玉成、王超，BP神经网络理论在模型修正中的应用，机械强度，1998
27．曹树谦、张文德、萧龙翔，振动结构模态分析-理论、实验与应用，天津大学出版社，2001
28．林循泓，振动模态参数识别及其应用，东南大学出版社，1990
29．[美]R．W．克拉夫、J．彭津著，结构动力学，科学出版社，1981
30．沈聚敏、王传志、江见鲸，钢筋混凝土有限元与板壳极限分析，清华大学出版社，1993
31．陆秋海，基于应变模态理论的结构修改和损伤神经网络辨识法研究，博士论文，1997
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2．董聪，基于动力特性的结构损伤定位方法，力学与实践，1999
3．李雪艳、刘济科、韩琳，结构损伤识别方法综述，第七届全国振动理论及其应用学术会议论文集，1999
4．苏娟、陆秋海、管迪华，神经网络法在定量损伤识别研究中的应用，清华大学学报，1999
5．崔飞、袁万城、史家钧，基于静态应变及位移测量的结构损伤识别法，同济大学学报，2000
6．陆秋海、李德葆、张维，利用模态实验参数识别结构损伤的神经网络法，工程力学，1999
7．郭国会、易伟建，基于神经网络的结构边界条件识别和损伤诊断，湖南大学学报，1998
8．苏娟、管迪华，利用神经网络分析及神经网络技术对结构损伤检测的探讨，机械强度，1999
9．易伟建、刘霞，结构损伤诊断的遗传算法研究，系统工程理论与实践，2001
10．王柏生、丁皓江，用于结构损伤识别的神经网络设计，工程设计，1999
11．于开平、邹经湘，模态参数识别的小波变换方法，宇航学报，1999
12．钟春香、杜文君，激励未知系统模态参数研究，华中理工大学学报，1994
13．冯文贤、陈新，基于实验复模态参数的有限元模型修正，航空学报，1999
14．刘福强、张令弥，一种改进的特征系统实现算法及在智能结构中的应用，振动工程，1999
15．秦权，桥梁结构的健康监测，中国公路学报，2000
16．李戈、秦权，董聪，用遗传算法选择悬索桥监测系统中传感器的最优布点，工程力学，2000
17．邓焱、严普强，桥梁结构损伤的振动模态检测，振动、测试与诊断，1999
18．秦权、张卫国，悬索桥的损伤识别，清华大学学报，1998
19．张启伟、袁万城、范立础，桥梁结构基于理论模型修正的损伤检测，1999
20．崔飞、袁万城、史家钧，传感器优化布设在桥梁健康监测中的应用，同济大学学报，1999
21．朱乐东，桥梁固有模态的识别，同济大学学报，1999
22．朱乐东、张之勇、张志成、朱宏平，香港汲水门大桥的模态识别，振动工程学报，1999
23．袁万城、崔飞、张启伟，桥梁健康监测与状态评估的研究现状与发展，同济大学学报，1999
24．范立础、袁万城、张启伟，悬索桥结构基于敏感性分析的动力有限元模型修正，土木工程学报，2000
25．李德葆，结构动力分析的应变模态法，机械强度，1990
26．蔡国平、赵玉成、王超，BP神经网络理论在模型修正中的应用，机械强度，1998
27．曹树谦、张文德、萧龙翔，振动结构模态分析-理论、实验与应用，天津大学出版社，2001
28．林循泓，振动模态参数识别及其应用，东南大学出版社，1990
29．[美]R．W．克拉夫、J．彭津著，结构动力学，科学出版社，1981
30．沈聚敏、王传志、江见鲸，钢筋混凝土有限元与板壳极限分析，清华大学出版社，1993
31．陆秋海，基于应变模态理论的结构修改和损伤神经网络辨识法研究，博士论文，1997
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