A Study of Machine Learning Techniques for Detecting and Classifying Structural Damage-Reference-Cited by-同舟云学术

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A Study of Machine Learning Techniques for Detecting and Classifying Structural Damage

Published:2015-08 Issue:4 Volume:5 Page:313-318
ISSN:2010-3700
Container-title:International Journal of Machine Learning and Computing
language:
Short-container-title:IJMLC

Author:

Nick William,Asamene Kassahun,Bullock Gina,Esterline Albert,Sundaresan Mannur

Publisher

EJournal Publishing

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications

Cited by 36 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder;Vehicle System Dynamics;2024-03-27

2. Comparative Study of Deep Learning Architectures for Textual Damage Level Classification;2024 11th International Conference on Signal Processing and Integrated Networks (SPIN);2024-03-21

3. Analysing Machine Learning Approaches for Lamb Wave-Based Damage Detection;Lecture Notes in Mechanical Engineering;2024

4. Bridge Safety State Classification Based on Unsupervised Machine Learning;Lecture Notes in Civil Engineering;2024

5. Multiclass Supervised Machine Learning Algorithms Applied to Damage and Assessment Using Beam Dynamic Response;Journal of Vibration Engineering & Technologies;2023-07-31

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