Damage Detection Method Using Support Vector Machine and First Three Natural Frequencies for Shear Structures

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Open Journal of Civil Engineering

ISSN Print: 2164-3164
ISSN Online: 2164-3172
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"Damage Detection Method Using Support Vector Machine and First Three Natural Frequencies for Shear Structures"
written by Hien HoThu, Akira Mita,
published by Open Journal of Civil Engineering, Vol.3 No.2, 2013

has been cited by the following article(s):

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[8]	Probabilistic modeling of discrete structural response with application to composite plate penetration models
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[9]	Damage identification of bridge structures considering temperature variations-based SVM and MFO
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[10]	An End-to-End Framework With Multisource Monitoring Data for Bridge Health Anomaly Identification
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[11]	Detection of structural damage using one-class support vector machine
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[12]	Vibration-based SHM strategy for a real time alert system with damage location and quantification
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[13]	A nonparametric unsupervised learning approach for structural damage detection
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[14]	Research Progress on Structural Damage Identification in Civil Engineering
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[15]	Bearing assessment tool for longitudinal bridge performance
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[16]	A data‐driven framework for near real‐time and robust damage diagnosis of building structures
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[17]	A damage classification approach for structural health monitoring using machine learning
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[18]	Promptly assessing probability of barge–bridge collision damage of piers through probabilistic-based classification of machine learning
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[19]	Structural health monitoring using wireless sensor networks: A comprehensive survey
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[20]	Modal identification of time-varying structures using the blind source separation techniques
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[21]	Machine learning algorithms for damage detection: Kernel-based approaches
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[22]	Structural Damage detection and classification based on Machine learning algorithms
	8th European Workshop On Structural Health Monitoring, 2016

[1]	Machine Learning Techniques for Structural Health Monitoring of Concrete Structures: A Systematic Review Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2023 DOI:10.1007/s40996-023-01054-5

[2]	An Integrated Method Based on Convolutional Neural Networks and Data Fusion for Assembled Structure State Recognition Sustainability, 2023 DOI:10.3390/su15076094

[3]	Machine Learning Techniques for Structural Health Monitoring of Concrete Structures: A Systematic Review Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2023 DOI:10.1007/s40996-023-01054-5

[4]	Development of Intelligent Technologies in SHM on the Innovative Diagnosis in Civil Engineering—A Comprehensive Review Buildings, 2023 DOI:10.3390/buildings13081903

[5]	Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures Applied Sciences, 2022 DOI:10.3390/app122110754

[6]	Structural Damage Detection Using Supervised Nonlinear Support Vector Machine Journal of Composites Science, 2021 DOI:10.3390/jcs5110303

[7]	Damage Identification of Bridge Structures Considering Temperature Variations-Based SVM and MFO Journal of Aerospace Engineering, 2021 DOI:10.1061/(ASCE)AS.1943-5525.0001225

[8]	Probabilistic Modeling of Discrete Structural Response with Application to Composite Plate Penetration Models Journal of Engineering Mechanics, 2021 DOI:10.1061/(ASCE)EM.1943-7889.0001996

[9]	An End-to-End Framework With Multisource Monitoring Data for Bridge Health Anomaly Identification IEEE Transactions on Instrumentation and Measurement, 2021 DOI:10.1109/TIM.2020.3013130

[10]	European Workshop on Structural Health Monitoring Lecture Notes in Civil Engineering, 2021 DOI:10.1007/978-3-030-64594-6_25

[11]	Bearing assessment tool for longitudinal bridge performance Journal of Civil Structural Health Monitoring, 2020 DOI:10.1007/s13349-020-00432-1

[12]	A data‐driven framework for near real‐time and robust damage diagnosis of building structures Structural Control and Health Monitoring, 2020 DOI:10.1002/stc.2488

[13]	Research Progress on Structural Damage Identification in Civil Engineering 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 2020 DOI:10.1109/ICITBS49701.2020.00076

[14]	A Damage Classification Approach for Structural Health Monitoring Using Machine Learning Complexity, 2018 DOI:10.1155/2018/5081283

[15]	Promptly assessing probability of barge–bridge collision damage of piers through probabilistic-based classification of machine learning Journal of Civil Structural Health Monitoring, 2017 DOI:10.1007/s13349-017-0208-9

[16]	Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey IEEE Communications Surveys & Tutorials, 2017 DOI:10.1109/COMST.2017.2691551

[17]	Machine learning algorithms for damage detection: Kernel-based approaches Journal of Sound and Vibration, 2016 DOI:10.1016/j.jsv.2015.11.008

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