Abstract
The present work evaluates the deep learning algorithm called Sparse Auto-Encoder (SAE) when applied to the characterization of structural anomalies. This study explores the SAE’s performance in a supervised damage detection approach to consolidate its application in the Structural Health Monitoring (SHM) field, especially when dealing with real-case structures. The main idea is to use the SAE to extract relevant features from the monitored signals and the well-known Support Vector Machine (SVM) to classify such characteristics within the context of an SHM problem. Vibration data from a numerical beam model and a highway viaduct in Brazil are considered to assess the proposed approach. In both analyzed examples, the efficiency of the implemented methodology achieved more than 99% of correct damage structural classifications, supporting the conclusion that SAE can extract relevant characteristics from dynamic signals that are useful for SHM applications.
Highlights
In structural systems, damage may be defined as a change that negatively affects the structure’s original performance
Most damage detection and health monitoring methods were mainly developed taking into account vibration signals monitored over time (i.g., time histories of accelerations, displacements, and velocities), as seen in the classic reference work of Doebling (1998) [4] and the presentday literature review made by Avci et al (2021) [5]
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Summary
Damage may be defined as a change that negatively affects the structure’s original performance. Understanding that the success of an unsupervised SHM approach initially requires an evaluation within the framework of supervised techniques, the objective of the present work is to evaluate the SAE algorithm to extract parameters from vibration signals, allowing the identification of structural alterations.
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