Bayesian estimation of output combining method for bridge damage identification using multiple CNNs
Author:
Affiliation:
1. Graduate School of Science and Technology, Gunma Univ
2. NOHMI BOSAI LTD.
Publisher
Japan Society of Mechanical Engineers
Link
https://www.jstage.jst.go.jp/article/transjsme/90/934/90_24-00037/_pdf
Reference10 articles.
1. Chul-Woo KIM, Kai-Chun CHANG, Sotaro KITAUCHI, Anomaly detection from changes in statistical patterns of structural dynamic characteristics of a simply supported steel-truss bridge, Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM)), Vol.70, No.2 (2014), I_63-I_72, DOI:10.2208/jscejam.70.I_63 (in Japanese)．
2. Fadel Yessoufoua, Jinsong Zhu, One-Class Convolutional Neural Network (OC-CNN) Model for Rapid Bridge Damage Detection Using Bridge Response Data, KSCE Journal of Civil Engineering (2023) Vol.27, No.4:1640-1660, DOI 10.1007/s12205-023-0063-7 (in Japanese)．
3. Hassan Alqahtani, Skanda Bharadwaj, Asok Raya, Classification of fatigue crack damage in polycrystalline alloy structures using convolutional neural networks, Engineering Failure Analysis Vol.119 (2021) 104908, DOI:10.1016/j.engfailanal.2020.104908 (in Japanese)．
4. Jihong Guo, Chao Liu, Jinfeng Cao, Dongxiang Jiang, Damage identification of wind turbine blades with deep convolutional neural networks, Renewable Energy Vol.174 (2021) 122-133, DOI:10.1016/j.renene.2021.04.040．
5. Jong Jae Leea, Chung Bang Yun, Damage diagnosis of steel girder bridges using ambient vibration data, Engineering Structures 28 (2006) 912-925, DOI:10.1016/j.engstruct.2005.10.017．
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