纤维增强复合材料梁的分层损伤识别

通过损伤发生前后结构频率的变化来检测和评估纤维增强复合材料(FRP)层合梁中的分层损伤。利用FRP层合梁在分层发生前后的一系列频率变化值,通过构建直观图像法和人工神经网络两种逆向算法反推出梁中分层损伤的三个参数,即分层所在的界面、位置和尺寸。为检验两种算法的有效性和识别精度,分别进行了理论和实验双重验证。理论验证结果表明,两种逆向检测算法都可以有效预测出分层损伤的三个参数,其中直观图像法相比人工神经网络预测精度更高。而实验验证采用文献中报道的实测频率,结果表明:直观图像法对于测量数据误差有更高的包容性,可以较为准确地预测出分层在FRP梁试件中的位置和大小;而人工神经网络对于实验误差相对敏感,对FRP梁试件中的分层损伤预测不够准确。综上认为,相较于人工神经网络,直观图像法具有更好的鲁棒性,应被优先选择应用于FRP梁的分层损伤识别。

VIBRATION-BASED DELAMINATION ASSESSMENT IN FIBER REINFORCED POLYMER BEAMS

Delaminations in fiber reinforced polymer (FRP) beams were detected and assessed through the changes in natural frequencies before and after the damage occurred in beams.To assess the three delamination parameters,namely,the interface,lengthwise location and size,two different inverse algorithms,i.e.graphical technique and artificial neural network (ANN),were developed to inversely predict the delamination from a series of known frequency shifts.To verify the prediction efficiency and accuracy of the inverse algorithms,both numerical and experimental validation were conducted and the results were compared.The numerical validation results show that both algorithms can predict the delamination parameters successfully,although the accuracy of the graphical technique is noticed to be higher than that of the ANN.Experimental validation using the measured frequencies in literature shows that the graphical technique can predict the delamination with satisfactory accuracy using the measured frequencies which is noise polluted,while ANN is very sensitive to the experimental errors and can hardly predict the delamination with experimental data.In conclusion,it is recommended to use the graphical technique rather than artificial neural network to assess the delamination in FRP beam through frequency shifts.