Anomaly Detection on Bridges Using Deep Learning With Partial Training-Reference-Cited by-同舟云学术

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Anomaly Detection on Bridges Using Deep Learning With Partial Training

Published:2024 Issue: Volume:12 Page:116530-116545
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Santos-Vila Ivan¹^ORCID,Soto Ricardo¹^ORCID,Vega Emanuel¹,Peña Fritz Alvaro²^ORCID,Crawford Broderick¹^ORCID

Affiliation:

1. Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

2. Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10643054.pdf?arnumber=10643054

Reference55 articles.

1. A New Approach to Damage Detection in Bridges Using Machine Learning

2. A data-based structural health monitoring approach for damage detection in steel bridges using experimental data

3. Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

4. A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit

5. A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection

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