Optimization of Nonlinear Temperature Gradient on Eigenfrequency Using Genetic Algorithm for Reinforced Concrete Bridge Structural Health-Reference-Cited by-同舟云学术

Optimization of Nonlinear Temperature Gradient on Eigenfrequency Using Genetic Algorithm for Reinforced Concrete Bridge Structural Health

Published:2019-08-09 Issue: Volume: Page:141-151
ISSN:2522-8595
Container-title:World Congress on Engineering and Technology; Innovation and its Sustainability 2018
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Author:

Concepcion Ronnie S.^ORCID,Ilagan Lorena C.,Valenzuela Ira C.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-20904-9_11

Reference22 articles.

1. Shi, A., & Yu, X. (2012). Structural damage detection using artificial neural networks and wavelet transform. IEEE Computational Intelligence for Measurement Systems and Applications.

2. Zhang, H., Gui, J., Xie, Y., & Sun, Y. (2013). Environmental effect removal based structural health monitoring in the internet of things. IEEE International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

3. Xia, Y., Chen, B., Weng, S., Ni, Y., & Xu, Y. (2012). Temperature effect on vibration properties of civil structures: A literature review and case studies. Hong Kong: The Hong Kong Polytechnic University.

4. Childs, D. (2016). The response of full bridge strain sensors to thermal variations. United Kingdom National Quantum.

5. El-Tayeb, E., El-Metwally, S., Askar, H., & Yousef, A. (2015). Thermal analysis of reinforced concrete beams and frames. Housing and Building National Research Center.

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