1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data
By Osama Abdeljaber, Onur Avcı, M. Kiranyaz, B. Boashash, Henry Sodano, Daniel J. Inman. Neurocomputing, 2018
475 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/406f1a630bd6a4839c91b34bfb1ac8a8bb7b82f8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- Serkan Kiranyaz
- Moncef Gabbouj
- Onur Avcı
- Osama Abdeljaber
- Armin Dadras Eslamlou
- Mohsen Azimi
- Gokhan Pekcan
- Turker Ince
In Control and Systems Engineering
Citations in the corpus
- Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, methodology): “[53] introduced a new approach that only required two states of damage, namely undamaged and fully-damaged cases, in order to alleviate the drawbacks.”
- 1D Convolutional Neural Networks and Applications: A Survey (2019, background): “in [52] developed a novel approach based on 1D CNNs, which require significantly less effort and labeled data for training.”
- A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, methodology): “The adaptive 1D CNN discussed [194] and [195] was processed in C++ installed on a personal computer with Intel ® OpenMP API, used for shared memory multiprocessing.”
- 1-D Convolutional Neural Networks for Signal Processing Applications (2019, background): “A paradigm shift has recently occurred with the studies [14], [18], [19], and [20], which have shown that 1D CNNs can achieve state-of-the-art damage detection accuracy in realtime and with the minimal training.”