Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold-Reference-Cited by-同舟云学术

Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold

Published:2021-05-05 Issue:9 Volume:36 Page:1150-1167
ISSN:1093-9687
Container-title:Computer-Aided Civil and Infrastructure Engineering
language:en
Short-container-title:Computer‐Aided Civil and Infrastructure Engineering

Author:

Sarmadi Hassan¹²^ORCID,Yuen Ka‐Veng³^ORCID

Affiliation:

1. Department of Civil Engineering Ferdowsi University of Mashhad Azadi Square Mashhad Iran

2. Head of Research and Development Ideh Pardazan Etebar Sazeh Fanavar Pooya (IPESFP) Company Mashhad Razavi Khorasan Iran

3. State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering University of Macau Macao China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12635

Reference52 articles.

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2. Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures

3. Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures

4. Structural health monitoring using extremely compressed data through deep learning

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