Structural damage identification under nonstationary excitations through recurrence plot and multi-label convolutional neural network-Reference-Cited by-同舟云学术

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Structural damage identification under nonstationary excitations through recurrence plot and multi-label convolutional neural network

Published:2021-12 Issue: Volume:186 Page:110101
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Li Dan,Liang Zhen-Long,Ren Wei-Xin,Yang Dong,Wang Shi-Dong,Xiang Shu-Lin

Funder

Science, Technology and Innovation Commission of Shenzhen Municipality

National Key Research and Development Program of China

Fundamental Research Funds for Central Universities of the Central South University

National Natural Science Foundation of China

Natural Science Foundation of Anhui Province

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference62 articles.

1. Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection;Sun;J. Struct. Eng.-ASCE,2020

2. Modeling and harnessing sparse and low-rank data structure: a new paradigm for structural dynamics, identification, damage detection, and health monitoring;Nagarajaiah;Struct. Control Health Monit.,2017

3. The state of the art of data science and engineering in structural health monitoring;Bao;Engineering,2019

4. Modal frequency-environmental condition relation development using long-term structural health monitoring measurement: Uncertainty quantification, sparse feature selection and multivariate prediction;Mu;Measurement,2018

5. Vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection;Magalhães;Mech. Syst. Signal Pr.,2012

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