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Surface Classification of Damaged Concrete Using Deep Convolutional Neural Network

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Abstract

Concrete is known for its a strength and durability as a building material. It is heavily utilized in almost all infrastructures, from pipes, building structures to roads and dams. However, due to external factors or internal compositions, concrete can be damaged and hence affects the quality of the constructions. The type of damage that appeared on concrete is often the first a clue as to how it occurred. Therefore proper diagnosing of the problem can help engineers determine how quickly and how best to fix it. The application of information technology, especially artificial intelligence, to automatically classify the damage types can help tremendously in this aspect. There have been some studies in using computer vision to examine the surfaces of concrete for damages. This study attempts a more challenging task of classifying the five common types of concrete damage. A new dataset is built and the Convolutional Neural Network (CNN) architecture is used for classification. The results obtained have an accuracy of 95 and 93% on the training set and the test set respectively.

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ACKNOWLEDGMENTS

We would like to thank our colleagues in the Information Technology Specialization Department, FPT University, Hanoi, Vietnam for their critical and relevant comments on the manuscript; Colleagues in the English Department who have helped to polish English text. We would also like to extend our gratitude to experts at the Vietnam Institute for Building Materials, for helping us with the methodological aspects of this study and with reviewing part of the data.

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Authors and Affiliations

  1. Department of Information Technology Specialization, FPT University, Hanoi, Vietnam

    P. D. Hung

  2. FPT University, Hanoi, Vietnam

    N. T. Su

  3. Hanoi University of Science and Technology, Hanoi, Vietnam

    V. T. Diep

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  1. P. D. Hung
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Correspondence to P. D. Hung.

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The authors declare that they have no conflicts of interest in the subject matter or materials discussed in this manuscript.

Additional information

Phan Duy Hung received his PhD degree from INP Grenoble France, in 2008.

From 2009 to now, he worked as a Lecturer, Head of Department, Director of Master Program in the Software engineering at FPT University, Hanoi, Vietnam.

His current research interests include Digita Signal and Image processing, Internet of Things, Bigdata and Artificial Intelligent, Measurement, Control systems and industry network.

Nguyen Tien Su received his B.Sc. from Hanoi University of Science and Technology, Vietnam in 2012.

From 2013 to now, he worked as an engineer in the fields: Machine Learning, Image Processing, Software engineering.

He is currently Master Student at FPT University.

Vu Thu Diep was born in 1985. She received a Master’s degree in 2010 and PhD’s degree in 2015 of Control and Automation of the Hanoi University of Science and Technology (HUST).

Since 2009, she has been a Lecturer at Hanoi University of Science and Technology. Her main research is in design and implementation of Automation, Measurement, Control systems and industry network; Machine Learning and Artificial Intelligent.

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Hung, P.D., Su, N.T. & Diep, V.T. Surface Classification of Damaged Concrete Using Deep Convolutional Neural Network. Pattern Recognit. Image Anal. 29, 676–687 (2019). https://doi.org/10.1134/S1054661819040047

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