Numerical and Experimental Evaluation of Structural Changes Using Sparse Auto-Encoders and SVM Applied to Dynamic Responses

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.

Damage identification using convolutional neural networks from instantaneous displacement measurements via image processing

“…5 artificial neural networks (ANNs) undoubtedly deserve to be highlighted among the machine learning algorithms. ANNs are extremely versatile and can be applied to SHM problems through various architectures, such as fully connected networks, 6,7 autoencoders, 8,9 and, more recently, convolutional neural networks (CNNs). 10,11 A CNN is a type of ANN commonly used in image recognition and processing tasks.…”