Hybrid Approach for Supervised Machine Learning Algorithms to Identify Damage in Bridges-Reference-Cited by-同舟云学术

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Hybrid Approach for Supervised Machine Learning Algorithms to Identify Damage in Bridges

Published:2024-08 Issue:8 Volume:29 Page:
ISSN:1084-0702
Container-title:Journal of Bridge Engineering
language:en
Short-container-title:J. Bridge Eng.

Author:

Bud Mihai Adrian¹^ORCID,Nedelcu Mihai²,Moldovan Ionut³^ORCID,Figueiredo Eloi⁴

Affiliation:

1. Ph.D. Candidate, Faculty of Civil Engineering, Technical Univ. of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania (corresponding author). ORCID: .

2. Associate Professor, Faculty of Civil Engineering, Technical Univ. of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania.

3. Associate Professor, Faculty of Engineering, Lusófona Univ., Campo Grande 376, 1749-024 Lisbon, Portugal. ORCID: .

4. Professor, Faculty of Engineering, Lusófona Univ., Campo Grande 376, 1749-024 Lisbon, Portugal.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JBENF2.BEENG-6537

Reference25 articles.

1. Bishop, C. M. 2006. Pattern recognition and machine learning. New York: Springer.

2. A Feature Extraction & Selection Benchmark for Structural Health Monitoring

3. Reliability of probabilistic numerical data for training machine learning algorithms to detect damage in bridges;Bud M. A.;Struct. Control Health Monit.,2022

4. CSI (Computers & Structures Inc.). 2016. CSI analysis reference manual for SAP2000, ETABS, SAFE and CSiBridge. Berkeley, CA: CSI.

5. Supervised deep learning with finite element simulations for damage identification in bridges;Fernandez-Navamuel A.;Eng. Struct.,2022

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