Ensemble Deep Learning Model for Damage Identification via Output-Only Signal Analysis

Abstract

Vibration-based methods have received considerable attention in structural condition monitoring applications. We have proposed a model to detect damaged points of a target structure using the GRU model and classify the 0.84 overall accuracy. To increase the model's accuracy in this research, we propose an ensemble deep learning model using LSTM and bi-directional LSTM incorporated with GRU. Each model predicted its RMSE trend and combined the damage estimation results from both models, which are mostly close to the true damage locations. As a result of synthesizing the three algorithms, the damage point of the cantilever beam was found with an accuracy of 0.88 and a misclassification rate of 0.12. The results indicate that the proposed combined approach provides enhanced reliability than a single algorithm.