Dynamic Response Recovery of Damaged Structures Using Residual Learning Enhanced Fully Convolutional Network

Abstract

Structural dynamic response corrupts frequently due to the sensor malfunction. The loss of dynamic response will hinder the structural condition assessment. In recent years, significant efforts have been devoted to recovering the dynamic response during the linear elastic stage of the structure. However, relevant researches on the response recovery of damaged structures are rarely reported due to its strong nonlinearity. With the growing significance of post-disaster structural maintenance, it is critical to develop effective methods for recovering missing data in damaged structures. To this end, this paper proposes a dynamic response recovery method for damaged structures using residual learning enhanced fully convolutional networks (FCN), which can provide a baseline for the recovery of monitoring data in operational civil infrastructure. Specifically, a FCN incorporating residual learning and skip connections is designed to capture high-dimensional nonlinear relationships between input and output channels, thereby achieving the data recovery for any concerned channel. Then, a time–frequency domain evaluation mode is constructed, in which L2 norm is used to measure the difference of recovery results in the time domain, while instantaneous frequency is employed to evaluate the integrity of the spectral information of recovery results. Finally, a destruction test of an experimental arch was conducted, and the acceleration data under different damaged state were collected to investigate the feasibility of the proposed method. Besides, the recovery effects concerning input channel location and quantity, multi-channel response and cross-state recovery are examined. The results show that even in a severely damaged state, the proposed method effectively recovers the missing data. In addition, improving the correlation between input and output channels and increasing the number of input channels can further enhance the recovery accuracy.