Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

By Osama Abdeljaber, Onur Avcı, Serkan Kiranyaz, Moncef Gabbouj, Daniel J. Inman. 2017

1,158 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/fd9564f2837d6dda62a3368576a064585f531fc6

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In Civil and Structural Engineering

In Mechanics of Materials

Citations in the corpus

Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning (2017, methodology): “D Convolutional Neural Network which obtains a classification accuracy of 97.4%; and Abdeljaber et al (2017) performed real-time damage detection using Convolutional Neural Network and showcased satisfactory efficiency.”
Deep Transfer Learning for Image‐Based Structural Damage Recognition (2018, methodology): “…a deep CNN to detect concrete cracks without calculating the defect features and proposed a region-based algorithm for detecting multiple damage types; Abdeljaber et al. (2017) implemented one dimensional (1D) CNN in vibration-based structural damage detection, veriﬁed experimentally by…”
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, methodology): “The data for study [185] is assembled and published on a public website by the authors [201].”
1-D Convolutional Neural Networks for Signal Processing Applications (2019, methodology): “Figure 4: Two pairs of sample vibration signals from normal (undamaged) and damaged structural joints [14].”
Structural damage identification based on autoencoder neural networks and deep learning (2018, background): “D Convolutional Neural Networks for detecting the structural damage has been conducted in 2017 [22].”
Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings (2017, methodology): “[19] used a CNN for structural damage detection on a grandstand simulator.”
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2020, methodology): “[108] proposed a direct damage identification method based on 1D CNN that can apply to the original environmental vibration signals.”
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, methodology): “[51] presented an approach for damage identification while using output-only response data.”