损伤位置分步识别法中参数选择、样本简化等问题的探究
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摘要
对于多层多跨的框架结构,构件数量众多,直接确定损伤位置十分困难。基于神经网络的损伤位置分步识别法是先构建一个网络来确定结构损伤层的位置,然后针对每层再分别构建一个网络,确定损伤层中损伤柱的编号,于是一个复杂的神经网络被分解简化为几个简单的子网络,这样可以根据模态参数的性质合理选择损伤识别指标,并降低训练样本的数量,从而提高了网络的识别精度。结合分步识别法,建立了详细的多层多跨框架结构数值模型,对模态参数的性质和选择,如何减少训练样本,样本的构造方法等影响识别的因素做了初步探究。
It is difficult to identify the damage location directly for the high frame structure which contains too many components. A method of damage location hierachical identification based on artificial neural networks for high frame structure is introduced. The main idea of this method can be described as follows: Firstly, a network should be designed to identify the damaged story. And then many networks are designed for any story to identify the damaged columns. In this way, a complex identification network is decompounded into some simple sub-networks. Meanwhile more rational identification index can be choose according to the character of modal parameter for each network. Therefore, amount of the train stylebook in hierachical identification are less than that of convention identification and the precision of identification is enhanced. With hierachical identification,a detailed high frame structure model is founded and choice of parameter, decrease of stylebook, method of stylebook make are studied.
It is difficult to identify the damage location directly for the high frame structure which contains too many components. A method of damage location hierachical identification based on artificial neural networks for high frame structure is introduced. The main idea of this method can be described as follows: Firstly, a network should be designed to identify the damaged story. And then many networks are designed for any story to identify the damaged columns. In this way, a complex identification network is decompounded into some simple sub-networks. Meanwhile more rational identification index can be choose according to the character of modal parameter for each network. Therefore, amount of the train stylebook in hierachical identification are less than that of convention identification and the precision of identification is enhanced. With hierachical identification,a detailed high frame structure model is founded and choice of parameter, decrease of stylebook, method of stylebook make are studied.
引文
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2Ceravolo R,Stefano A De,Sabia D.Hierarchical use of neural techniques in structural damage recognition.Smart Mateials and Structures,1995;4:270—280
3陈素文,李国强.基于BP网络的框架结构损伤的多重分步识别理论.地震工程与工程震动,2002;22(5):18—23
4瞿伟廉,陈伟,等.基于神经网络技术的复杂框架结构节点损伤的两步诊断法.土木工程学报,2003;36(5):37—44
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6姜绍飞.基于神经网络的结构优化与损伤检测.北京:科学出版社,2002:134—140
7姜绍飞,等.基于概率神经网络的悬索桥损伤定位研究.东北大学学报,2002;23(5):480—483
8Specht D F.Probabilistic neural networks.Neural Networks,1990,3:109—118
9Kaminski P C.The approximation location of damage through the analysis of natural frequencies with artificial neural networks.Journal of Process Mechanical Engineering,1995;209(E2):117—224
10Lam H F,Ko J M,Wong C W.Localization of damaged structural connections based on experimental modal and sensitivity analysis.Journal sound and Vibration,1998;210(1):91—115
2Ceravolo R,Stefano A De,Sabia D.Hierarchical use of neural techniques in structural damage recognition.Smart Mateials and Structures,1995;4:270—280
3陈素文,李国强.基于BP网络的框架结构损伤的多重分步识别理论.地震工程与工程震动,2002;22(5):18—23
4瞿伟廉,陈伟,等.基于神经网络技术的复杂框架结构节点损伤的两步诊断法.土木工程学报,2003;36(5):37—44
5Friswell M I,Penny J E T.The practical limits of damage detection and location using vibration data.11th VPI&SU Symposium on Structural Dynamics and Control,Blacksburg,Virginia,USA:VPI&SU,1997:31—40
6姜绍飞.基于神经网络的结构优化与损伤检测.北京:科学出版社,2002:134—140
7姜绍飞,等.基于概率神经网络的悬索桥损伤定位研究.东北大学学报,2002;23(5):480—483
8Specht D F.Probabilistic neural networks.Neural Networks,1990,3:109—118
9Kaminski P C.The approximation location of damage through the analysis of natural frequencies with artificial neural networks.Journal of Process Mechanical Engineering,1995;209(E2):117—224
10Lam H F,Ko J M,Wong C W.Localization of damaged structural connections based on experimental modal and sensitivity analysis.Journal sound and Vibration,1998;210(1):91—115
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