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Structural Health Monitoring

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Showing 101–125 of 197 papers

TitleStatusHype
A Meta-Learning Approach to Population-Based Modelling of Structures—0
B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization—0
Conditional deep generative models as surrogates for spatial field solution reconstruction with quantified uncertainty in Structural Health Monitoring applications—0
Quantification of Damage Using Indirect Structural Health Monitoring—0
Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring—0
Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral MappingCode0
Balanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime—0
Deep learning for structural health monitoring: An application to heritage structures—0
Improving aircraft performance using machine learning: a review—0
Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials—0
Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine—0
Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature—0
On topological data analysis for structural dynamics: an introduction to persistent homology—0
On topological data analysis for SHM; an introduction to persistent homology—0
A topological analysis of cointegrated data: a Z24 Bridge case study—0
On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation—0
Physics-informed machine learning for Structural Health Monitoring—0
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
On statistic alignment for domain adaptation in structural health monitoringCode0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic—0
Lost Vibration Test Data Recovery Using Convolutional Neural Network: A Case Study—0
Modelling variability in vibration-based PBSHM via a generalised population form—0
Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health MonitoringCode0
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