# A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications

arXiv: https://arxiv.org/abs/2004.04373 · HTML: https://mlsanity.com/paper/2004.04373/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 30 of 33)

- [IDRLnet: A Physics-Informed Neural Network Library](https://mlsanity.com/paper/2107.04320/) (2021)
- [CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference](https://mlsanity.com/paper/2310.14608/) (2023)
- [CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection](https://mlsanity.com/paper/2202.07831/) (2022)
- [A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic](https://mlsanity.com/paper/2204.06362/) (2022)
- [Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection](https://mlsanity.com/paper/2112.03478/) (2021)
- [CNN-DST: ensemble deep learning based on Dempster-Shafer theory for vibration-based fault recognition](https://mlsanity.com/paper/2110.07191/) (2021)
- [Neuro-symbolic model for cantilever beams damage detection](https://mlsanity.com/paper/2305.03063/) (2023)
- [System-reliability based multi-ensemble of GAN and one-class joint Gaussian distributions for unsupervised real-time structural health monitoring](https://mlsanity.com/paper/2102.01158/) (2021)
- [Generative Adversarial Networks for Data Generation in Structural Health Monitoring](https://mlsanity.com/paper/2112.08196/) (2021)
- [Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptation](https://mlsanity.com/paper/2508.07049/) (2025)
- [A Novel Approach for Deterioration and Damage Identification in Building Structures Based on Stockwell-Transform and Deep Convolutional Neural Network](https://mlsanity.com/paper/2111.06155/) (2021)
- [Assessing Trustworthiness of Autonomous Systems](https://mlsanity.com/paper/2305.03411/) (2023)
- [Neural operator for structural simulation and bridge health monitoring](https://mlsanity.com/paper/2305.07889/) (2023)
- [On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach](https://mlsanity.com/paper/2403.17181/) (2024)
- [A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration Response](https://mlsanity.com/paper/2412.02643/) (2024)
- [Leveraging systems' non-linearity to tackle the scarcity of data in the design of Intelligent Fault Diagnosis Systems](https://mlsanity.com/paper/2606.20323/) (2026)
- [LLM-Based Test Oracles: Source-of-Authority Taxonomy -- A Systematic Literature Review](https://mlsanity.com/paper/2607.05031/) (2026)
- [Wave based damage detection in solid structures using artificial neural networks](https://mlsanity.com/paper/2103.16339/) (2021)
- [Vibration-Based Condition Monitoring By Ensemble Deep Learning](https://mlsanity.com/paper/2110.06601/) (2021)
- [Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring](https://mlsanity.com/paper/2212.14048/) (2022)
- [A new transformation for embedded convolutional neural network approach toward real-time servo motor overload fault-detection](https://mlsanity.com/paper/2304.04005/) (2023)
- [Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios](https://mlsanity.com/paper/2308.14632/) (2023)
- [Unveiling the frontiers of deep learning: innovations shaping diverse domains](https://mlsanity.com/paper/2309.02712/) (2023)
- [Deep Learning in Earthquake Engineering: A Comprehensive Review](https://mlsanity.com/paper/2405.09021/) (2024)
- [Deep learning architectures for data-driven damage detection in nonlinear dynamic systems](https://mlsanity.com/paper/2407.03700/) (2024)
- [Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems](https://mlsanity.com/paper/2408.08629/) (2024)
- [DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection](https://mlsanity.com/paper/2412.04781/) (2024)
- [Real-Time Structural Deflection Estimation in Hydraulically Actuated Systems Using 3D Flexible Multibody Simulation and DNNs](https://mlsanity.com/paper/2503.07528/) (2025)
- [Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification](https://mlsanity.com/paper/2511.00100/) (2025)
- [A convolutional neural network deep learning method for model class selection](https://mlsanity.com/paper/2511.03743/) (2025)

## Abstract (arXiv)

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.