Instance-level recognition and quantification for concrete surface bughole based on deep learning-Reference-Cited by-同舟云学术

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Instance-level recognition and quantification for concrete surface bughole based on deep learning

Published:2019-11 Issue: Volume:107 Page:102920
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Wei Fujia,Yao Gang,Yang Yang,Sun Yujia

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference43 articles.

1. State of the Art - Quality of Concrete Surfaces;Vikan,2007

2. Design and optimization of an instrument for measuring bughole rating of concrete surfaces;Ozkul;Journal of the Franklin Institute,2011

3. Effect of untreated surface disbonds on performance of FRP-retrofitted concrete beams;Kalayci;J. Compos. Constr.,2009

4. The influence of air content and fluidity of mortar on the characteristics of surface voids in self-compacting concrete;Ichimiya;Doboku Gakkai Ronbunshu,2002

5. P. Samuelsson, Voids in concrete surfaces, Journal Proceedings 67 (11) (1970) 868–874. doi:10.14359/7316.

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