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Structural Health Monitoring Based on Data Science Techniques

1st Edition 2022, 2021

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Data-driven cymbal bronze alloy identification via evolutionary machine learning with automatic feature selection

Journal of intelligent manufacturing, 2024, Vol.35 (1), p.257-273

2024

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A two-step technique for damage assessment using numerical and experimental vibration data

Structural health monitoring, 2011-07, Vol.10 (4), p.417-428

2011

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Machine learning-based classification of bronze alloy cymbals from microphone captured data enhanced with feature selection approaches

Expert systems with applications, 2023-04, Vol.215, p.119378, Article 119378

2023

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A Fuzzy Approach to Drum Cymbals Classification

Revista IEEE América Latina, 2022-09, Vol.20 (9), p.2172-2180

2022

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Physics-Informed Machine Learning for Structural Health Monitoring

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.347-367

2022

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On Explicit and Implicit Procedures to Mitigate Environmental and Operational Variabilities in Data-Driven Structural Health Monitoring

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.309-330

2022

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Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responses

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.117-142

2022

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Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.45-73

2022

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Explainable Artificial Intelligence to Advance Structural Health Monitoring

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.331-346

2022

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Predictive Monitoring of Large-Scale Engineering Assets Using Machine Learning Techniques and Reduced-Order Modeling

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.185-205

2022

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Applications of Deep Learning in Intelligent Construction

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.227-245

2022

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Vibration-Based Damage Feature for Long-Term Structural Health Monitoring Under Realistic Environmental and Operational Variability

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.289-307

2022

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Population-Based Structural Health Monitoring

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.413-435

2022

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Partially Supervised Learning for Data-Driven Structural Health Monitoring

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.389-411

2022

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Environmental Influence on Modal Parameters: Linear and Nonlinear Methods for Its Compensation in the Context of Structural Health Monitoring

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.269-288

2022

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Interpretable Machine Learning for Function Approximation in Structural Health Monitoring

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.369-388

2022

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A Self-adaptive Hybrid Model/data-Driven Approach to SHM Based on Model Order Reduction and Deep Learning

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.165-184

2022

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Machine Learning-Based Structural Damage Identification Within Three-Dimensional Point Clouds

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.437-456

2022

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Fault Diagnosis in Structural Health Monitoring Systems Using Signal Processing and Machine Learning Techniques

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.143-164

2022

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Bayesian Deep Learning for Vibration-Based Bridge Damage Detection

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.27-43

2022

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New Sensor Nodes, Cloud, and Data Analytics: Case Studies on Large Scale SHM Systems

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.457-484

2022

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Vibration-Based Structural Damage Detection Using Sparse Bayesian Learning Techniques

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.1-25

2022

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Integrated SHM Systems: Damage Detection Through Unsupervised Learning and Data Fusion

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.247-268

2022

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Development and Validation of a Data-Based SHM Method for Railway Bridges

Structural Health Monitoring Based on Data Science Techniques, 2022, Vol.21, p.95-116

2022

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Structural Health Monitoring Based on Data Science Techniques

Data-driven cymbal bronze alloy identification via evolutionary machine learning with automatic feature selection

A two-step technique for damage assessment using numerical and experimental vibration data

Machine learning-based classification of bronze alloy cymbals from microphone captured data enhanced with feature selection approaches

A Fuzzy Approach to Drum Cymbals Classification

Physics-Informed Machine Learning for Structural Health Monitoring

On Explicit and Implicit Procedures to Mitigate Environmental and Operational Variabilities in Data-Driven Structural Health Monitoring

Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responses

Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning

Explainable Artificial Intelligence to Advance Structural Health Monitoring

Predictive Monitoring of Large-Scale Engineering Assets Using Machine Learning Techniques and Reduced-Order Modeling

Applications of Deep Learning in Intelligent Construction

Vibration-Based Damage Feature for Long-Term Structural Health Monitoring Under Realistic Environmental and Operational Variability

Population-Based Structural Health Monitoring

Partially Supervised Learning for Data-Driven Structural Health Monitoring

Environmental Influence on Modal Parameters: Linear and Nonlinear Methods for Its Compensation in the Context of Structural Health Monitoring

Interpretable Machine Learning for Function Approximation in Structural Health Monitoring

A Self-adaptive Hybrid Model/data-Driven Approach to SHM Based on Model Order Reduction and Deep Learning

Machine Learning-Based Structural Damage Identification Within Three-Dimensional Point Clouds

Fault Diagnosis in Structural Health Monitoring Systems Using Signal Processing and Machine Learning Techniques

Bayesian Deep Learning for Vibration-Based Bridge Damage Detection

New Sensor Nodes, Cloud, and Data Analytics: Case Studies on Large Scale SHM Systems

Vibration-Based Structural Damage Detection Using Sparse Bayesian Learning Techniques

Integrated SHM Systems: Damage Detection Through Unsupervised Learning and Data Fusion

Development and Validation of a Data-Based SHM Method for Railway Bridges

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