International Conference on Construction and Real Estate Management 2016
Correction of the Temperatures Measured by Infrared Thermography Based on Neural Networks
Publication: ICCREM 2016: BIM Application and Off-Site Construction
ABSTRACT
As a way of nondestructive testing, infrared thermography has been increasingly used in the field of building. However, it was found that temperature measured by thermographic camera will deviate from accurate values, and the experimental data in this paper show that the lower the temperature of target surface is, the larger the deviation amplitude will be. To solve this problem, two correction identification models based on RBF and BP neural network were, respectively, constructed with MATLAB. The temperature data measured by infrared thermography was used as input variable, while the data measured by thermocouple was used as output. Five types of building materials were selected as the testing targets. The results show that the identification accuracy of networks is related to the number of training samples, the more the training samples, the higher accuracy the RBF network will have. At last, one complete system which can correct the temperatures measured by infrared thermography based on RBF neural network was established.
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ACKNOWLEDGMENTS
This study was financially supported by the National Natural Science Foundation of China (No. 51478136).
REFERENCES
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Information & Authors
Information
Published In
ICCREM 2016: BIM Application and Off-Site Construction
Pages: 213 - 221
Editors: Yaowu Wang, Ph.D., Professor, Harbin Institute of Technology, Mohamed Al-Hussein, Ph.D., Professor, University of Alberta, Geoffrey Q. P. Shen, Ph.D., Professor, The Hong Kong Polytechnic University, and Yimin Zhu, Ph.D., Professor, Louisiana State University
ISBN (Online): 978-0-7844-8027-4
Copyright
© 2017 American Society of Civil Engineers.
History
Published online: Aug 14, 2017
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