Structural damage identification under nonstationary excitations through recurrence plot and multi-label convolutional neural network

Abstract

Civil engineering structures inevitably suffer from nonstationary ambient excitations in practice, which make conventional damage identification methods relying on the stationary assumption ineffective. This study presents a novel method based on unthresholded assembled recurrence distance matrix (UARDM) and multi-label convolutional neural network (CNN) for structural damage identification under nonstationary excitations. UARDM is a new type of recurrence plot (RP) that is proposed to integrate information of multiple channels and dispense with the artificially selected threshold. It reveals intrinsic dynamic characteristics of the structure using its vibration responses from the perspective of global probabilistic autocorrelation. After that, CNN is applied to automatically extract damage-sensitive features of UARDMs and classify them for the identification of damage cases. Instead of the traditional single-label CNN model that labels each combination of damage location and level as an objective class, the multi-label CNN model is developed to decouple the identification processes of damage locations and levels in order to improve the identification accuracy and computational efficiency. It evaluates the damage level at each location through a sub-branch with an independent set of labels and detects the damage locations by fusing information of all the sub-branches. A comprehensive comparison was conducted among single-label and multi-label CNN models input with raw accelerations, unthresholded multivariate recurrence plots (UMRPs), unthresholded recurrence plots (URPs) and UARDMs through numerical simulation and experimental test. It was demonstrated that the proposed structural damage identification method based on UARDM and multi-label CNN was able to identify multiple damage locations and levels under various stationary and nonstationary excitations with higher accuracy, efficiency and robustness, and even able to detect multiple-damage cases that were not measured beforehand and involved in the training dataset.