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损伤指标
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    In order to identify the structure damage location and overcome the disadvantages of BP network,such as being affected greatly by ill samples and having poor antinoise ability,SOFM(Self-Organizing Feature Map)network is adopted to identify the damage location in a truss structure.
    为实现结构的损伤定位,克服BP神经网络受病态样本影响大且抗噪声能力弱等缺点,采用SOFM(Self-Organizing Feature Map)网络,以结构振动组合损伤指标为网络输入,对一桁架结构进行了损伤位置识别研究。
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  damage index
A damage index, used to identify possible locations and corresponding severities of local damage, is defined and expressed in terms of modal displacements that are obtained numerically from mode shapes of the undamaged and the damaged structures.
      
Based on a small damage assumption, a damage index equation is revealedn the context of elasticity.
      
It turns out that the resulting damage index equation inherently suffers from singularities near inflection nodes.
      
With the aid of the singular value decomposition technique, the solution to the damage index equation is achieved in the Haar wavelet space.
      
Next, the physical desired solution to the damage index equation is reconstructed from the one in the wavelet space.
      
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A two-stage damage diagnosis method was developed to evaluate the structural health of various types of buildings.A normalized damage index was used in the first stage to locate possibly damaged elements.The damage extent was estimated in the second stage using the second-order eigen-sensitivity approximation method.Four known damage cases(3 symmetric and 1 asymmetric) for braced structures were analyzed for the analytical benchmark problems proposed by the International Association for Structural Control and...

A two-stage damage diagnosis method was developed to evaluate the structural health of various types of buildings.A normalized damage index was used in the first stage to locate possibly damaged elements.The damage extent was estimated in the second stage using the second-order eigen-sensitivity approximation method.Four known damage cases(3 symmetric and 1 asymmetric) for braced structures were analyzed for the analytical benchmark problems proposed by the International Association for Structural Control and the American Society of Civil Engineers(IASC-ASCE) Task Group on Structural Health Monitoring.The results indicate that this method effectively identifies the location and severity of the damage in all the benchmark problems.

为准确地评估结构健康状况,提出了一种基于结构振动特性的两阶段损伤诊断方法。由归一化的损伤指标确定可能出现损伤的单元位置,采用二阶特征灵敏度分析法确定可能损伤单元的损伤程度。考虑3种对称损伤模式和1种非对称损伤模式,对由国际结构控制协会与美国土木工程学会(IA SC-A SCE)提出的健康监测B enchm ark模型结构第二阶段有斜撑的情况进行模拟分析。结果表明:该方法能够有效地识别出结构损伤单元的位置与严重程度。

In order to identify the structure damage location and overcome the disadvantages of BP network,such as being affected greatly by ill samples and having poor antinoise ability,SOFM(Self-Organizing Feature Map)network is adopted to identify the damage location in a truss structure.The identification results show that SOFM network can distinguish similar samples and give intuitionistic judgement of the identification results through the topology map.When the noise is removed,the damage location can be identified...

In order to identify the structure damage location and overcome the disadvantages of BP network,such as being affected greatly by ill samples and having poor antinoise ability,SOFM(Self-Organizing Feature Map)network is adopted to identify the damage location in a truss structure.The identification results show that SOFM network can distinguish similar samples and give intuitionistic judgement of the identification results through the topology map.When the noise is removed,the damage location can be identified correctly.When the noise level is under 30%,all the elements except individual element can be identified correctly in a probability over 95%,which shows that the SOFM network has a powerful antinoise ability.

为实现结构的损伤定位,克服BP神经网络受病态样本影响大且抗噪声能力弱等缺点,采用SOFM(Self-Organizing Feature Map)网络,以结构振动组合损伤指标为网络输入,对一桁架结构进行了损伤位置识别研究。识别结果显示SOFM网络能够区分相似样本,由网络的拓扑图可以直观地评价网络训练和识别结果。在不考虑噪声情况下,网络可以正确识别损伤位置,在噪声水平不大于30%情况下,除个别单元外,网络对其他单元损伤的正确识别率均高于95%,显示出很好的抗噪声能力。

 
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