7 Vibration-based damage detection using a novel hybrid CNN-SVM approach
-
Shahin Ghazvineh
Abstract
In classic machine learning-based damage detection algorithms, extracting damage-sensitive features from time series is a challenging issue. Also, this paradigm can delay processing procedures and requires preprocessing. Many efforts have been made to overcome this limitation by expanding deep learning (DL) in structural health monitoring (SHM). However, because most of these systems require considerable measurements during the training step, they are unsuitable for real-time applications. To solve the challenges above, we offer a robust approach using two-dimensional convolutional neural networks (CNNs) and support vector machines (SVMs), merging feature extraction and a rapid classifier at the same time. The method employs a shallow CNN network that receives raw acceleration signals. Both noisy and noise-free datasets are used to verify the hybrid CNN-SVM approach. The results showed an increase in robustness, speed efficiency, and accuracy over traditional machine learning approaches. The results proved efficient, making the algorithm reliable even under high noise conditions.
Abstract
In classic machine learning-based damage detection algorithms, extracting damage-sensitive features from time series is a challenging issue. Also, this paradigm can delay processing procedures and requires preprocessing. Many efforts have been made to overcome this limitation by expanding deep learning (DL) in structural health monitoring (SHM). However, because most of these systems require considerable measurements during the training step, they are unsuitable for real-time applications. To solve the challenges above, we offer a robust approach using two-dimensional convolutional neural networks (CNNs) and support vector machines (SVMs), merging feature extraction and a rapid classifier at the same time. The method employs a shallow CNN network that receives raw acceleration signals. Both noisy and noise-free datasets are used to verify the hybrid CNN-SVM approach. The results showed an increase in robustness, speed efficiency, and accuracy over traditional machine learning approaches. The results proved efficient, making the algorithm reliable even under high noise conditions.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Editors’ short biography IX
- 1 Seismic site investigation and structural amplification based on geotechnical and structural health monitoring 1
- 2 Impact of industry 4.0 technologies on structural health monitoring 29
- 3 Health monitoring of recycled aggregates-reinforced concrete beams retrofitted by concrete jacket using piezoelectric transducers 63
- 4 Identification of critical response of bilinear-hysteretic SDOF model with tuned inertial mass damper under long-duration ground motion through internal simulation monitoring 79
- 5 Damage identification using physics-based datasets: From convolutional to metric-informed damage-sensitive feature extractors 101
- 6 Structural health monitoring of steel plates using modified modal strain energy indicator and optimization algorithms 125
- 7 Vibration-based damage detection using a novel hybrid CNN-SVM approach 137
- 8 Fast probabilistic damage detection using inverse surrogate models 159
- 9 Remote sensing techniques for post-disaster infrastructure health monitoring 197
- 10 Recent developments in the building information modeling-based programs used for structural and architectural purposes 215
- 11 Evaluating the current state of digitalisation of the UK construction industry 237
- Index 259
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Editors’ short biography IX
- 1 Seismic site investigation and structural amplification based on geotechnical and structural health monitoring 1
- 2 Impact of industry 4.0 technologies on structural health monitoring 29
- 3 Health monitoring of recycled aggregates-reinforced concrete beams retrofitted by concrete jacket using piezoelectric transducers 63
- 4 Identification of critical response of bilinear-hysteretic SDOF model with tuned inertial mass damper under long-duration ground motion through internal simulation monitoring 79
- 5 Damage identification using physics-based datasets: From convolutional to metric-informed damage-sensitive feature extractors 101
- 6 Structural health monitoring of steel plates using modified modal strain energy indicator and optimization algorithms 125
- 7 Vibration-based damage detection using a novel hybrid CNN-SVM approach 137
- 8 Fast probabilistic damage detection using inverse surrogate models 159
- 9 Remote sensing techniques for post-disaster infrastructure health monitoring 197
- 10 Recent developments in the building information modeling-based programs used for structural and architectural purposes 215
- 11 Evaluating the current state of digitalisation of the UK construction industry 237
- Index 259
