Generative Adversarial Networks for Labelled Vibration Data Generation Article Swipe

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Furkan Luleci , F. Necati Çatbaş , Onur Avcı ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2112.08195

As Structural Health Monitoring (SHM) being implemented more over the years, the use of operational modal analysis of civil structures has become more significant for the assessment and evaluation of engineering structures. Machine Learning (ML) and Deep Learning (DL) algorithms have been in use for structural damage diagnostics of civil structures in the last couple of decades. While collecting vibration data from civil structures is a challenging and expensive task for both undamaged and damaged cases, in this paper, the authors are introducing Generative Adversarial Networks (GAN) that is built on the Deep Convolutional Neural Network (DCNN) and using Wasserstein Distance for generating artificial labelled data to be used for structural damage diagnostic purposes. The authors named the developed model 1D W-DCGAN and successfully generated vibration data which is very similar to the input. The methodology presented in this paper will pave the way for vibration data generation for numerous future applications in the SHM domain.

Related Topics

Generative Grammar

Rayon

Computer Science

Convolutional Neural Network

Deep Learning

Vibration

Artificial Intelligence

Generative Adversarial Network

Machine Learning

Modal Analysis

Structural Engineering

Mathematical Analysis

Concepts

Generative grammar Modal Structural health monitoring Computer science Adversarial system Convolutional neural network Deep learning Vibration Artificial intelligence Task (project management) Domain (mathematical analysis) Generative adversarial network Artificial neural network Machine learning Modal analysis Structural engineering Engineering Systems engineering Acoustics Mathematics Chemistry Polymer chemistry Physics Mathematical analysis

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2112.08195
PDF: https://arxiv.org/pdf/2112.08195
OA Status: green
Cited By: 2
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2112.08195

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Title: Generative Adversarial Networks for Labelled Vibration Data Generation

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Language: en

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Publication year: 2021

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Publication date: 2021-12-07

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Authors: Furkan Luleci, F. Necati Çatbaş, Onur Avcı

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Concepts: Generative grammar, Modal, Structural health monitoring, Computer science, Adversarial system, Convolutional neural network, Deep learning, Vibration, Artificial intelligence, Task (project management), Domain (mathematical analysis), Generative adversarial network, Artificial neural network, Machine learning, Modal analysis, Structural engineering, Engineering, Systems engineering, Acoustics, Mathematics, Chemistry, Polymer chemistry, Physics, Mathematical analysis

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Cited by: 2

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Citations by year (recent): 2025: 1, 2023: 1

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abstract_inverted_index.Structural	1
abstract_inverted_index.algorithms	39
abstract_inverted_index.artificial	103
abstract_inverted_index.assessment	26
abstract_inverted_index.collecting	58
abstract_inverted_index.diagnostic	112
abstract_inverted_index.evaluation	28
abstract_inverted_index.generating	102
abstract_inverted_index.generation	147
abstract_inverted_index.structural	45, 110
abstract_inverted_index.structures	19, 50, 63
abstract_inverted_index.Adversarial	84
abstract_inverted_index.Wasserstein	99
abstract_inverted_index.challenging	66
abstract_inverted_index.diagnostics	47
abstract_inverted_index.engineering	30
abstract_inverted_index.implemented	6
abstract_inverted_index.introducing	82
abstract_inverted_index.methodology	135
abstract_inverted_index.operational	14
abstract_inverted_index.significant	23
abstract_inverted_index.structures.	31
abstract_inverted_index.applications	151
abstract_inverted_index.successfully	123
abstract_inverted_index.Convolutional	93
cited_by_percentile_year.max	95
cited_by_percentile_year.min	89
countries_distinct_count	0
institutions_distinct_count	3
citation_normalized_percentile.value	0.4902485
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False