A framework of structural damage detection for civil structures using a combined multi-scale convolutional neural network and echo state network-Reference-Cited by-同舟云学术

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A framework of structural damage detection for civil structures using a combined multi-scale convolutional neural network and echo state network

Published:2022-01-13 Issue: Volume: Page:
ISSN:0177-0667
Container-title:Engineering with Computers
language:en
Short-container-title:Engineering with Computers

Author:

He Yingying,Zhang Likai,Chen Zengshun,Li Cruz Y.

Funder

the Natural Science Foundation of Chongqing, China

Fundamental Research Funds for the Central Universities of China

Chongqing Engineering and Technology Research Center for Big Data of Public Transportation Operation

the Science and Technology Research Program of Chongqing Municipal Education Commission

the Key project of Technological Innovation and Application Development in Chongqing

the Scientific Research Program of Chongqing College of Humanities, Science & Technology

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,General Engineering,Modeling and Simulation,Software

Link

https://link.springer.com/content/pdf/10.1007/s00366-021-01584-4.pdf

Reference30 articles.

1. Brownjohn JMW (2007) Structural health monitoring of civil infrastructure. Philos Trans R Soc A Math Phys Eng Sci 365:589–622. https://doi.org/10.1098/rsta.2006.1925

2. Rahul V, Alokita S, Jayakrishna K et al (2019) Structural health monitoring of aerospace composites. Structural health monitoring of biocomposites, fibre-reinforced composites and hybrid composites. Elsevier, New York, pp 33–52

3. Xiang J, Yang Z, Aguilar J (2018) Structural health monitoring for mechanical structures using multi-sensor data. Int J Distrib Sens Netw 14:25

4. An Y, Spencer B, Ou J (2015) A test method for damage diagnosis of suspension bridge suspender cables. Comput Aided Civ Infrastruct Eng 30:771–784

5. Gul M, Catbas F (2011) Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering. J Sound Vib 330:1196–1210

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