An Overview of Deep Learning Methods Used in Vibration-Based Damage Detection in Civil Engineering
Author | Avci O. |
Author | Abdeljaber O. |
Author | Kiranyaz, Mustafa Serkan |
Available date | 2022-04-26T12:31:17Z |
Publication Date | 2022 |
Publication Name | Conference Proceedings of the Society for Experimental Mechanics Series |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1007/978-3-030-77143-0_10 |
Abstract | This paper presents a brief overview of vibration-based damage identification studies based on Deep Learning (DL) in civil engineering structures. The presence, type, size, and propagation of structural damage on civil infrastructure have always been a topic of research. In the last couple of decades, there has been a significant shift in the damage detection paradigm when the advancements in sensing and computing technologies met with the ever-expanding use of artificial neural network algorithms. Machine-Learning (ML) tools enabled researchers to implement more feasible and faster tools in damage detection applications. When an artificial neural network has more than three layers, it is typically considered as a ?deep? learning network. Being an important accomplishment of the ML era, DL tools enable complex systems which are made of several layers to learn implementations of data with outstanding categorization and compartmentalization capability. In fact, with proper training, a DL tool can operate directly with the unprocessed raw data and help the algorithm produce output data. Competitive capabilities like this led DL algorithms perform very well in complicated problems by dividing a relatively large problem into much smaller and more manageable portions. Specifically for damage identification and localization on civil infrastructure, Convolutional Neural Networks (CNNs) and Unsupervised Pretrained Networks (UPNs) are the known DL tools published in the literature. This paper presents an overview of these studies. |
Language | en |
Publisher | Springer |
Subject | Backpropagation Convolutional neural networks Deep learning Multilayer neural networks Structural dynamics Structural health monitoring Civil engineering structures Civil infrastructures Damage Identification Damage localization Deep learning Infrastructure health Learning methods Learning tool Machine-learning Vibration-based damage detection Damage detection |
Type | Conference Paper |
Pagination | 93-98 |
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Electrical Engineering [2616 items ]