This paper presents a new approach for automatical classification of structural state through deep learning. In this work, a Convolutional Neural Network (CNN) was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state. The digital image correlation (DIC) technology was utilized to collect vibration information of an actual steel frame, and subsequently, the raw signals, without further pre-processing, were directly utilized as the CNN samples. The results show that CNN can achieve 99% classification accuracy for the research model. Besides, compared with the backpropagation neural network (BPNN), the CNN had an accuracy similar to that of the BPNN, but it only consumes 19% of the training time. The outputs of the convolution and pooling layers were visually displayed and discussed as well. It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals and classify them; 2) the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN; 3) the CNN has better anti-noise ability.

本文提出了一种通过深度学习自动分类结构状态的新方法。在这项工作中，设计了一个卷积神经网络 (CNN)，将特征提取和分类块融合到一个智能紧凑的学习系统中，并检测钢框架的结构状态；输入是一系列振动信号，输出是结构状态。利用数字图像相关（DIC）技术采集实际钢架的振动信息，然后将原始信号直接用作CNN样本，无需进一步预处理。结果表明，CNN对于研究模型可以达到99%的分类准确率。此外，与反向传播神经网络（BPNN）相比，CNN 具有与 BPNN 相似的准确度，但它只消耗了 19% 的训练时间。卷积层和池化层的输出也被直观地显示和讨论。证明：1）CNN可以从振动信号中提取结构状态信息并进行分类；2）CNN对不完整数据的检测和计算性能优于BPNN；3）CNN具有更好的抗噪能力。