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- Title
A Novel Approach for Structural Damage Detection Using Multi-Headed Stacked Deep Ensemble Learning.
- Authors
Asghari, Arghavan; Ghodrati Amiri, Gholamreza; Darvishan, Ehsan; Asghari, Arian
- Abstract
Purpose: Damage detection of civil infrastructures is vital in structural health monitoring; however, traditional methods are time-consuming and sometimes require skilled experts. With the development of machine learning and deep learning techniques, different artificial neural networks are being used to detect structural damages. However, individual networks can have errors in detecting damages and estimating the accuracy of predictions. Thus, ensemble learning techniques play an essential role in providing more reliable results by combining results obtained by individual models. In this study, a novel deep ensemble learning approach is proposed based on the stacked generalization method for detecting damages in the case of structural health monitoring. A distinguishing feature of the proposed approach is the utilization of a multi-headed deep artificial neural network architecture for classification problems, with a neural network as a meta-learner. Method: In the first step, the CNN, CAE, and LSTM models are trained separately to use the latter as input data for the meta-learner. Then, a deep multi-headed MLP neural network is designed as a meta-learner to combine predictions using the stacking ensemble method to enhance the result accuracy. Results: The proposed method is verified by three structures prepared for structural health monitoring validation. The results represent the promising scope of stacking ensemble learning applications for structural health monitoring and damage detection. Conclusion: The findings indicate that employing ensemble learning is more precise than a neural network when it comes to identifying structural damages.
- Subjects
ARTIFICIAL neural networks; DEEP learning; MULTILAYER perceptrons; STRUCTURAL health monitoring; MACHINE learning; INFRASTRUCTURE (Economics)
- Publication
Journal of Vibration Engineering & Technologies, 2024, Vol 12, Issue 3, p4209
- ISSN
2523-3920
- Publication type
Article
- DOI
10.1007/s42417-023-01116-y