Rapid early damage detection using transmissibility with distance measure analysis under unknown excitation in long-term health monitoring

Abstract

Accurate and efficient damage detection in long-term health monitoring for structures still encounters many difficulties due to the effect of environment. Furthermore, recorded big data requires efficient damage detection algorithm. In this study, an efficient and effective damage detection algorithm is proposed using transmissibility along with Mahalanobis distance and Hotelling T-square. A numerically simulated beam and an experimentally tested laboratory structure are used to validate the proposed algorithm. Results demonstrate good performance of the proposed technique in damage detection.

A Novelty Detection Approach for Tendons of Prestressed Concrete Bridges Based on a Convolutional Autoencoder and Acceleration Data

“…Many studies are based on the application of analytical methods such as MSD (e.g., [18,19,20,21,22]) or ANN (e.g., [23,24,25,26,27,28,29,30,31]) to achieve a higher accuracy of novelty detection using damage-sensitive features. The MSD-based method describes the detection of novelty data by setting up a category for normal data and detecting outliers.…”

“…Mosavi et al [20] showed that damage localization of a steel beam using MSD is possible when using vector autoregressive model coefficients to generate acceleration data of a simple steel beam structure. Zhou and Wahab [21] developed a faster novelty detection approach using MSD, where the transmissibility of the structures is affected by unknown excitation. Zhou et al [22] extracted PCA features based on a frequency response function calculated using response data and used it to detect the damage (anomalies) of the structure with MSD.…”