大跨度异型钢结构多点损伤检测方法

The Detection Method of Multi-Point Damage for Large-Span Special-Type Steel Structure

在钢结构检测过程中,由于损伤识别参数选择中存在差异,导致钢结构损伤检测结果误差较大。因此,提出大跨度异型钢结构多点损伤检测方法。利用有限元模型在大跨度钢结构中选择最佳的传感器放置点,依据获取的钢结构信息,确定适当损伤识别参数。通过构建曲率模态损伤定位模型,初步确定大跨度异型钢结构多点损伤位置。借助PNN神经网络完成大跨度异型钢结构多点损伤检测。实验结果表明:所设计检测方法与传统方法相比较将检测误差分别降低了23.09%、45.34%,提升了大跨度异型钢结构损伤检测的精度。

In the process of steel structure detection,due to the difference in the selection of damage identification parameters,the results deviation of damage detection of steel structure is large.Therefore,a detection method of multi-point damage is proposed for large-span steel structures.Using the finite element model,the best sensor placement point is selected in the long-span steel structure,and the appropriate damage identification parameters are determined according to the obtained steel structure information.By constructing the curvature mode damage location model,the multi-point damage location of long-span special-type steel structure is preliminarily determined.With the help of PNN neural network,the multi-point damage detection of large-span steel structure with dissimilarity is completed.The experimental results show that,compared with the traditional method,the designed detection method reduces the detection deviation by 23.09%and 45.34%respectively,and improves the detection accuracy of large-span special-type steel structure.