基于改进均匀设计表框架结构损伤识别方法与实验验证

为对框架结构柱破坏进行无损识别,提出一种基于改进均匀设计表确定结构损伤样本数据库,使用神经网络与平面单元模态应变能变化率进行损伤定位和程度识别的方法。提出应用正交设计优化均匀设计,以解决均匀设计试验点过少的缺陷。该方法以平面单元模态应变能变化率作为损伤指标,采用改进均匀设计表,选择具有代表性的损伤工况作为广义回归神经网络(general regression neural network,GRNN)的训练样本,对损伤位置进行识别;在确定损伤位置的前提下,利用径向基(radical basis function,RBF)神经网络对损伤程度进行识别。通过分步方法确定框架柱构件的损伤位置与损伤程度。数值模拟与试验验证了所提出方法的有效性。平面单元模态应变能变化率识别指标克服了空间结构模态振型不完备的缺陷,两步识别法避免神经网络训练时不收敛、趋于局部最小值等缺陷。该方法可用于框架结构柱损伤的位置确定和损伤程度识别。

Damage identification method and experimental verification of frame column based on improved uniform test design

To non-destructive detect the damage of column members in frame structures, this study proposes a new method to determine the location and degree of damage, which by using neural network and Rate of modal strain energy change of plane element (PMSECR) based on the damage sample database by an improved uniform design table. This study solves the defect of too few uniform design test points by using orthogonal design to optimize the uniform design table. The two-step method is used to determine the damage location and degree of frame columns. Firstly, to identify the damage location, the PMSECR is taken as damage index, and the representative damage condition is selected as a training sample of General Regression Neural Network (GRNN) by improved uniform test table. Then, after determining the damage location, the Radical Basis Function Neural Network (RBFNN) is used to identify the degree of damage. The effectiveness of the proposed method is verified by numerical simulation and experiments. The identification index of PMSECR overcomes the defect of incomplete modal of structures, and the two-step identification method avoids the defects of non-convergence and a local minimum in the training of neural network. The method can be used to locate the damage location and identify the damage degree of frame columns.