A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications
By Onur Avcı, Osama Abdeljaber, Serkan Kiranyaz, Mohammed Hussein, Moncef Gabbouj, Daniel J. Inman. arXiv.org, 2020
1,195 citations recorded by the source corpus.
Abstract Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures; maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Damage may accumulate on structures due to different environmental and human-induced factors. Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Considerable effort has been put into vibration-based methods, which utilize the vibration response of the monitored structure to assess its condition and identify structural damage. Meanwhile, with emerging computing power and sensing technology in the last decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have become more feasible and extensively used in vibration-based structural damage detection with elegant performance and often with rigorous accuracy. While there have been multiple review studies published on vibration-based structural damage detection, there has not been a study where the transition from traditional methods to ML and DL methods are described and discussed. This paper aims to fulfill this gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
Subfields: Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/af810cacb9992794e0247487a76d377d8e52dc25
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
- Turker Ince
- Daniel J. Inman
- Arman Malekloo
- Mark A. Girolami
Citations in the corpus
- Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, positive): “In another work by Avci et al. 231 (Level 1-2) , SOM was applied on a grid structure based on the stiffness reduction and boundary condition changes to identify and quantify the damage.”
- 1D Convolutional Neural Networks and Applications: A Survey (2019, methodology): “With emerging computing power, vibration-based techniques have become feasible, proved capable and widely used for structural damage detection [97].”