Correlation-based damage detection method using convolutional neural network for civil infrastructure. (1st July 2023)

• Record Type: Journal Article
• Authors: Pamuncak, Arya; Zivanovic, Stana; Adha, Augusta; Liu, Jingliang; Laory, Irwanda
• Is Part Of: Computers & structures. Volume 282(2023)
• Publication Date: 2023-07-01
• Subjects: Convolutional neural network -- Unsupervised learning -- Vibration-based method; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2023.107034
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: A novel damage detection method corCNN method is presented. The novel CorCNN method reduced the complexity of analysis by enabling feature extraction. The CorCNN employs an unsupervised learning method, therefore no data labelling is required. The novel CorCNN outperformed other correlation-based methods in vibration-based damage detection. Abstract: We present a novel damage detection method named CorCNN that utilizes one-dimensional convolutional neural networks to detect damage based on observed changes in correlation between measurements. CNN architecture is used in the method to automatically extract important information from raw measurement data. A CNN model is trained in an unsupervised manner, eliminating the need for data labeling. An assessment of structural responses to a 20 m full-scale bridge in healthy and damaged conditions is conducted to validate the method. For the investigated problem, hyperparameters are optimised to find the optimal combination. To detect the presence of damage, residuals derived from the discrepancies between the actual data and prediction are analyzed. Additionally, CorCNN is compared to other machine learning methods, including linear regression, artificial neural networks, and random forests, using the given dataset. According to the results, the CorCNN method outperforms other machine learning models in detecting damage to the structure.