Abstract

Previously, it was nearly impossible to use raw time series sensory signals for structural health monitoring due to the inherent high dimensionality of measured data. However, recent developments in deep learning techniques have overcome the need of complex preprocessing in time series data. This study extends the applicability of four prominent deep learning algorithms: Multi-Layer Perceptron, Long Short Term Memory network, 1D Convolutional Neural Network, and Convolutional Neural Network to structural damage detection using raw data. Three structures ranging from relatively small structures to considerably large structures are extensively investigated, i.e., 1D continuous beam under random excitation, a 2D steel frame subjected to earthquake ground motion, and a 3D stayed-cable bridge under vehicular loads. In addition, a modulated workflow is designed to ease the switch of different DL algorithms and the fusion of data from sensors. The results provide a more insightful picture of the applicability of Deep Learning algorithms in performing structural damage detection via quantitative evaluations of detection accuracy, time complexity, and required data storage in multi-damage scenarios. Moreover, these results emphasize the high reliability of 2DCNN, as well as the good balance between accuracy and complexity of Long Short Term Memory and 1D Convolutional Neural Network.

摘要

以前，由于测量数据固有的高维性，几乎不可能使用原始时间序列感官信号进行结构健康监测。然而，深度学习技术的最新发展已经克服了对时间序列数据进行复杂预处理的需求。本研究扩展了四种著名深度学习算法的适用性：多层感知器、长短期记忆网络、一维卷积神经网络和卷积神经网络，以使用原始数据进行结构损伤检测。广泛研究了从相对较小的结构到相当大的结构的三种结构，即随机激励下的一维连续梁、受到地震地面运动的二维钢框架和车辆载荷下的三维斜拉索桥。此外，调制工作流旨在简化不同深度学习算法的切换和来自传感器的数据融合。结果通过对检测精度、时间复杂度和多损伤场景中所需数据存储的定量评估，更深入地了解深度学习算法在执行结构损伤检测方面的适用性。此外，这些结果强调了 2DCNN 的高可靠性，以及 Long Short Term Memory 和 1D Convolutional Neural Network 的准确性和复杂性之间的良好平衡。以及在多损伤场景中所需的数据存储。此外，这些结果强调了 2DCNN 的高可靠性，以及 Long Short Term Memory 和 1D Convolutional Neural Network 的准确性和复杂性之间的良好平衡。以及在多损伤场景中所需的数据存储。此外，这些结果强调了 2DCNN 的高可靠性，以及 Long Short Term Memory 和 1D Convolutional Neural Network 的准确性和复杂性之间的良好平衡。