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A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure

Mechanical systems and signal processing, 2021-08, Vol.157, p.107735, Article 107735

2021

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Model error effects in supervised damage identification of structures with numerically trained classifiers

Mechanical systems and signal processing, 2023-02, Vol.184, p.109741, Article 109741

2023

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Generalization gap estimation for damage classification tasks when finite element simulated data is used for training: A numerical study and experimental validation on a steel truss

Engineering structures, 2024-11, Vol.319, p.118855, Article 118855

2024

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Model-based damage identification with simulated transmittance deviations and deep learning classification

Structural health monitoring, 2022-09, Vol.21 (5), p.2206-2230

2022

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Structural Health Monitoring using deep learning with optimal finite element model generated data

Mechanical systems and signal processing, 2020-11, Vol.145, p.106972, Article 106972

2020

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Machine learning based condition monitoring for gear transmission systems using data generated by optimal multibody dynamics models

Mechanical systems and signal processing, 2023-05, Vol.190, p.110130, Article 110130

2023

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Parametric Study on Structural Damage Classification with Numerically Simulated Vibration Data

Journal of physics. Conference series, 2024-06, Vol.2647 (2), p.22004

2024

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Stochastic multiscale modeling of graphene reinforced composites

Engineering structures, 2017-08, Vol.145, p.176-189

2017

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From Binary to Multi-Class: Neural Networks for Structural Damage Classification in Bridge Monitoring Under Static and Dynamic Loading

Dynamics, 2024-10, Vol.4 (4), p.786-803

2024

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Production of porous copper with high surface area for efficient water purification

CIRP journal of manufacturing science and technology, 2016-05, Vol.13, p.85-89

2016

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Yield locus prediction using statistical and RVE-based fast Fourier transform crystal plasticity models and validation for drawing steels

Journal of physics. Conference series, 2018-07, Vol.1063 (1), p.12051

2018

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Damage Identification of Structures Through Machine Learning Techniques with Updated Finite Element Models and Experimental Validations

Model Validation and Uncertainty Quantification, Volume 3, 2020, p.143-154

2020

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Estimation of Numerical Data Reliability in Damage Detection Tasks

Proceedings of the 10th International Operational Modal Analysis Conference (IOMAC 2024), 2024, p.394-401

2024

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Transmittance Anomalies for Model-Based Damage Detection with Finite Element-Generated Data and Deep Learning

Data Science in Engineering, Volume 9, 2022, p.41-50

2022

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Machine Learning-Based Condition Monitoring with Multibody Dynamics Models for Gear Transmission Faults

Data Science in Engineering, Volume 9, 2022, p.51-59

2022

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A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure

Model error effects in supervised damage identification of structures with numerically trained classifiers

Generalization gap estimation for damage classification tasks when finite element simulated data is used for training: A numerical study and experimental validation on a steel truss

Model-based damage identification with simulated transmittance deviations and deep learning classification

Structural Health Monitoring using deep learning with optimal finite element model generated data

Machine learning based condition monitoring for gear transmission systems using data generated by optimal multibody dynamics models

Parametric Study on Structural Damage Classification with Numerically Simulated Vibration Data

Stochastic multiscale modeling of graphene reinforced composites

From Binary to Multi-Class: Neural Networks for Structural Damage Classification in Bridge Monitoring Under Static and Dynamic Loading

Production of porous copper with high surface area for efficient water purification

Yield locus prediction using statistical and RVE-based fast Fourier transform crystal plasticity models and validation for drawing steels

Damage Identification of Structures Through Machine Learning Techniques with Updated Finite Element Models and Experimental Validations

Estimation of Numerical Data Reliability in Damage Detection Tasks

Transmittance Anomalies for Model-Based Damage Detection with Finite Element-Generated Data and Deep Learning

Machine Learning-Based Condition Monitoring with Multibody Dynamics Models for Gear Transmission Faults

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