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Open AccessFeature PaperArticle

Deep Learning-Based Damage, Load and Support Identification for a Composite Pipeline by Extracting Modal Macro Strains from Dynamic Excitations

1
International Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, China
2
Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
3
Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
4
Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan 98167-45845, Iran
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2564; https://doi.org/10.3390/app8122564
Received: 20 November 2018 / Revised: 4 December 2018 / Accepted: 7 December 2018 / Published: 10 December 2018
(This article belongs to the Special Issue Structural Damage Detection and Health Monitoring)
Modal macro strain-based damage identification is a promising approach since it has the advantages of high sensitivity and effectiveness over other related methods. In this paper, a basalt fiber-reinforced polymer (BFRP) pipeline system is used for analysis by using long-gauge distributed fiber Bragg grating (FBG) sensors. Dynamic macro strain responses are extracted to form modal macro strain (MMS) vectors. Both longitudinal distribution and circumferential distribution plots of MMS are compared and analyzed. Results show these plots can reflect damage information of the pipeline based on the previous work carried out by the authors. However, these plots may not be good choices for accurate detection of damage information since the model is 3D and has different flexural and torsional effects. Therefore, by extracting MMS information in the circumferential distribution plots, a novel deep neural network is employed to train and test these images, which reflect the important and key information of modal variance in the pipe system. Results show that the proposed Deep Learning based approach is a promising way to inherently identify damage types, location of the excitation load and support locations, especially when the structural types are complicated and the ambient environment is changing. View Full-Text
Keywords: modal macro strain; fiber-reinforced polymer (FRP) composite pipeline; damage; load; support identification; deep learning; convolutional neural network modal macro strain; fiber-reinforced polymer (FRP) composite pipeline; damage; load; support identification; deep learning; convolutional neural network
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Zhao, Y.; Noori, M.; Altabey, W.A.; Ghiasi, R.; Wu, Z. Deep Learning-Based Damage, Load and Support Identification for a Composite Pipeline by Extracting Modal Macro Strains from Dynamic Excitations. Appl. Sci. 2018, 8, 2564.

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