Convolutional neural networks for automated damage recognition and damage type identification-Reference-Cited by-同舟云学术

Convolutional neural networks for automated damage recognition and damage type identification

Published:2018-07-10 Issue:10 Volume:25 Page:e2230
ISSN:1545-2255
Container-title:Structural Control and Health Monitoring
language:en
Short-container-title:Struct Control Health Monit

Author:

Modarres Ceena¹,Astorga Nicolas²,Droguett Enrique Lopez¹²^ORCID,Meruane Viviana²

Affiliation:

1. Mechanical Engineering Department; University of Maryland; College Park Maryland USA

2. Mechanical Engineering Department; University of Chile; Santiago Chile

Funder

Chilean National Fund for Scientific and Technological Development (Fondecyt)

Petroleum Institute, Abu Dhabi, UAE

Publisher

Wiley

Subject

Mechanics of Materials,Building and Construction,Civil and Structural Engineering

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/stc.2230/fullpdf

Reference42 articles.

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3. An innovative methodology for detection and quantification of cracks through incorporation of depth perception;Jahanshahi;Mach. Vis. Appl.,2013

4. Robust automated concrete damage detection algorithms for field applications;Lattanzi;J. Comput. Civ. Eng.,2012

5. Bridge related damage quantification using unmanned aerial vehicle imagery;Ellenberg;Struct Control Health Monit.,2016

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