Structural health monitoring of infrastructure especially bridges plays a vital role in post-earthquake recovery. Coupling emerging techniques in machine learning with structural health monitoring can provide unprecedented tools for damage detection and identification. This paper explores the use of time-series acceleration or displacement data collected from a shake-table experiment of a two-span bridge utilizing pretensioned rocking columns to predict the damage state of each bridge bent, where the major identified damage was the fracture of the longitudinal bars. To overcome the limitation of small data size collected during the shake-table test that hindered the use of artificial neural networks and recurrent neural networks, the time-series data were encoded into images using three methods Gramian angular summation field, Gramian angular difference field, and Markov transition field. Then, the encoded images were used as an input for convolutional neural network models. Three different data entries for the input layers were used including encoded images from recorded accelerations, drift ratios, and both. Two training/testing scenarios were proposed to test the efficacy of the convolutional neural networks. Convolutional neural network models trained on Markov transition field encoded images from acceleration performed with 100% accuracy during the training phase and more than 94% for the testing phase.

中文翻译：

---

基于卷积神经网络的摇桥系统结构健康监测通过将时间序列编码为图像

基础设施（尤其是桥梁）的结构健康监测在震后恢复中发挥着至关重要的作用。将机器学习中的新兴技术与结构健康监测相结合，可以为损伤检测和识别提供前所未有的工具。本文探讨了使用从两跨桥梁的振动台实验中收集的时间序列加速度或位移数据，该桥梁利用预张拉摇杆来预测每个桥梁弯曲的损坏状态，其中主要确定的损坏是桥梁的断裂。纵向钢筋。为了克服在振动台测试期间收集的小数据量的限制，阻碍了人工神经网络和递归神经网络的使用，使用三种方法将时间序列数据编码成图像格拉米角求和场，Gramian 角差场和马尔可夫跃迁场。然后，编码的图像被用作卷积神经网络模型的输入。输入层使用了三个不同的数据条目，包括来自记录的加速度、漂移率和两者的编码图像。提出了两种训练/测试场景来测试卷积神经网络的功效。在马尔可夫过渡场编码图像上训练的卷积神经网络模型在训练阶段以 100% 的准确率执行，在测试阶段以超过 94% 的准确率执行。提出了两种训练/测试场景来测试卷积神经网络的功效。在马尔可夫过渡场编码图像上训练的卷积神经网络模型在训练阶段以 100% 的准确率执行，在测试阶段以超过 94% 的准确率执行。提出了两种训练/测试场景来测试卷积神经网络的功效。在马尔可夫过渡场编码图像上训练的卷积神经网络模型在训练阶段以 100% 的准确率执行，在测试阶段以超过 94% 的准确率执行。