基于门控循环单元神经网络的箱型梁结构裂纹损伤检测方法

GRU neural network-based method for box girder crack damage detection

[目的]随着智能船舶的发展,传统裂纹损伤检测方法已难以满足检测需求,为此,提出一种基于门控循环单元(GRU)神经网络的箱型梁结构裂纹损伤实时检测方法。[方法]通过基于Python语言的ABAQUS二次开发技术,建立箱型梁裂纹损伤模型,计算其在动态高斯白噪声激励下的加速度响应。通过数据裁剪技术扩充原始数据之后生成数据集,并考虑噪声的影响。建立基于GRU的箱型梁裂纹损伤检测模型,直接将加速度响应数据集作为输入,以最小损失函数值为目标来训练模型,并与基于小波包变换的多层感知机神经网络(WPT-MLP)进行对比。[结果]结果显示,所提出的GRU模型在损伤位置和损伤长度的检测上相比WPT-MLP检测精度更高,对噪声的敏感程度更低,且在对损伤位置的近似预测方面精度也较高。[结论]研究证明了GRU神经网络在包含多个板的箱型梁结构裂纹损伤检测中的适用性。

[Objectives]With the development of intelligent ships,it has been difficult for traditional crack damage detection methods to meet the detection requirements.This paper proposes a real-time crack damage detection method for box girders based on a gated recurrent unit(GRU)neural network.[Methods]Using the secondary development technology of Abaqus based on the Python language,a box girder crack damage model is built,and its acceleration response under dynamic Gaussian white noise excitation is calculated.A dataset is generated by expanding the original data using the data cropping method,and the influence of noise is considered.A box girder crack damage detection model based on GRU is established,the acceleration response dataset is directly used as input and the minimum loss function value is used as a target to train the model.This method is then compared to the wavelet packet transform-based multi-layer perceptron(WPTMLP)model.[Results]The comparison shows that the GRU model proposed in this paper has higher detection accuracy than the WPT-MLP model in damage location and extent detection.It is less sensitive to noise and has higher accuracy in approximate prediction.[Conclusions]The results of this study verify the applicability of GRU neural networks in the crack damage detection of box girders containing multiple plates.