The wavelet transform as a Gaussian process for damage detection-Reference-Cited by-同舟云学术

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The wavelet transform as a Gaussian process for damage detection

Published:2017-08-25 Issue:2 Volume:25 Page:e2087
ISSN:1545-2255
Container-title:Structural Control and Health Monitoring
language:en
Short-container-title:Struct Control Health Monit

Author:

Balafas Konstantinos¹^ORCID,Kiremidjian Anne S.¹,Rajagopal Ram¹

Affiliation:

1. Department of Civil and Environmental Engineering; Stanford University; Stanford CA 94305 USA

Funder

National Science Foundation and the Network for Earthquake Engineering Simulation (NEES)

Stanford Graduate Fellowship program

John A. Blume Earthquake Engineering Research Center

Publisher

Wiley

Subject

Mechanics of Materials,Building and Construction,Civil and Structural Engineering

Reference35 articles.

1. C. R. Farrar H. Sohn Pattern recognition for structural health monitoring Las Vegas, NV, USA 2000

2. Damage diagnosis using time series analysis of vibration signals

3. Structural Health Monitoring Using Statistical Pattern Recognition Techniques

4. Damage prognosis: the future of structural health monitoring

5. M. L. Fugate H. Sohn C. R. Farrar Unsupervised learning methods for vibration-based damage detection 2000

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1. Damage detection of bridges based on spectral sub-band features and hybrid modeling of PCA and KPCA methods;STRUCT MONIT MAINT;2022

2. Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data;Journal of Data Science;2022

3. Damage detection and localization of a steel truss bridge model subjected to impact and white noise excitations using empirical wavelet transform neural network approach;Measurement;2021-11

4. Damage detection and characterization of a scaled model steel truss bridge using combined complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification approach;Structural Health Monitoring;2021-09-28

5. Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study;Structural Health Monitoring;2021-05-31

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