5 Damage identification using physics-based datasets: From convolutional to metric-informed damage-sensitive feature extractors
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Matteo Torzoni
Abstract
Two alternative strategies addressing damage identification in structural health monitoring are presented in this contribution. Both strategies rely on reduced data representations - or features - to enable damage identification from vibrational data. To exploit a supervised learning scheme, training datasets are generated through numerical simulations, possibly speeded up through reduced order modelling. The first strategy deals with damage identification as a classification task employing onedimensional convolutional neural networks. Despite the good performance displayed in the proposed numerical benchmark of an eight-storey building, this approach suffers from the need of defining the possible damage classes a-priori, and from the lack of robustness of the extracted features. Both issues are successfully addressed by a second strategy, which relies on a Siamese architecture to learn a damage-sensitive low-dimensional metric space. In this second case, damage identification can be performed by solving a regression task in the learned metric space. This second approach is assessed against a test case involving a railway bridge, displaying impressive damage localization capabilities.
Abstract
Two alternative strategies addressing damage identification in structural health monitoring are presented in this contribution. Both strategies rely on reduced data representations - or features - to enable damage identification from vibrational data. To exploit a supervised learning scheme, training datasets are generated through numerical simulations, possibly speeded up through reduced order modelling. The first strategy deals with damage identification as a classification task employing onedimensional convolutional neural networks. Despite the good performance displayed in the proposed numerical benchmark of an eight-storey building, this approach suffers from the need of defining the possible damage classes a-priori, and from the lack of robustness of the extracted features. Both issues are successfully addressed by a second strategy, which relies on a Siamese architecture to learn a damage-sensitive low-dimensional metric space. In this second case, damage identification can be performed by solving a regression task in the learned metric space. This second approach is assessed against a test case involving a railway bridge, displaying impressive damage localization capabilities.
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
