神经网络在桁架结构损伤检测中的应用

Neural networks applied to girder construction fault detection

结构损伤导致其振动频率的变化,测量结构振动频率可以判断是否存在损伤;基于频率测量的结构损伤识别方法具有测试简单、精度较好的优点。在分析结构固有频率的基础上,用模态参数构造对损伤敏感的标识量,并将其作为特征参数输入到神经网络中实现损伤识别。结合煤矿皮带走廊桁架结构损伤的特点,利用神经网络技术,提出了一种对煤矿中常见的皮带走廊桁架结构锈蚀损伤的检测方法,以期为同类型结构的损伤检测提供参考。

Structure damage detection method based on frequency measurement exhibits a feature that natural frequencies of structure can be measured conveniently with relatively high precision. Damage detection by model parameters needs to solve intricate mathematical iteration problem, which makes it difficult to realize real-time mapping ability, which can change inverts problem into forward problem. Based on the point, damage features formed by vibration modal parameters are inputted to neural network as parameters for structural health monitoring. In the paper, a special method was used to detect damage of truss, considering the damage type. The results as given in conclusion show preliminarily that our method is feasible.