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A deep neural network for electrical resistance calibration of self-sensing carbon fiber polymer composites compatible with edge computing structural monitoring hardware electronics
1,3 Department of Computer Sciences, Universidad Alcalá, Madrid, Spain
2 Civil Engineering Department, University of Minho, 4800 Guimarães, Portugal
The self-sensing ability of materials, in particular carbon fiber polymer composites (SSCFPC), is a must-have requirement when designing a structural monitoring network for remote assessment of structural serviceability. This work presents a study using an Artificial Deep Neural Network (ADNN) where is evaluated the electrical resistance (R) output of specimens subjected to an unchanged deformation state of 2,86% strain for prolonged periods of time.
Six ADNN architectures are evaluated with varying numbers of neurons on pre-defined hidden layers, sharing the same four data inputs and one output. The dataset is based on 3,276 data points collected during the experimental campaign of an innovative electrode design embedded in SSCFPC specimens. The effect of the number of iterations and the architecture of the neural network is investigated in proposed ADNN models. Simple moving average (SMA), and moving Standard Deviation, are determined and plotted in terms of z-score to assist in performance evaluation of proposed ADNN models.
The optimal ADNN architecture is found among six proposed architectures and for each of the four SSCFPC mixtures. Results show the proposed model architectures are able to predict values of R with greater accuracy than traditional regression mathematical methods when traditional statistical coefficients are used. However, when analyzing data in a time-series manner results show further research is needed to achieve optimal accuracy results. The analysis presented focused on the structural monitoring network infrastructure and hardware electronics compatibility for further development of this type of SSCFPC as a self-sensing composite material with the ability of automatic calibration and suitable for real-time data acquisition and artificial intelligence modeling.
Keywords: Carbon Fiber Composites, Edge Computing, Machine Learning, Self-Sensing, Stress Relaxation, Temperature dependence
Suggested Citation Tomás M., Jalali S., Tabatha K. A deep neural network for electrical resistance calibration of self-sensing carbon fiber polymer composites compatible with edge computing structural monitoring hardware electronics. J. Structural Health Monitoring. 2024; 23(2) : 750-775. doi:10.1177/14759217231170001
Status: published Date: 26-05-2023 *Corresponding author: +32 471 632 520 (WhatsApp only)
short URL address to this document here on GitHub: https://tinyurl.com/SmartCarbonFiberComposites
Data Availability
Data used to support the findings of this study are available for anyone to download and use on the following dataverse: https://dataverse.harvard.edu/dataverse/selfSensingCarbonfiberComposites. PCB electronics hardware design KiCad files are available on GitHub with a Creative Commons License: https://github.com/aeonSolutions/openScience-Smart-Device-for-Structural-Self-Sensing-Carbon-Fiber-Based-Composites-/tree/main
Declaration of Competing Interest
To this date, the author declares to have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
About the Author
Miguel Tomás has been researching and implementing technology solutions for start-up businesses and public institutions for the past 20 years. To learn more about the author, connect to his LinkedIn profile page using the following web address: https://www.linkedin.com/in/migueltomas/. Visit my publications page to find out more about published scientific communications in peer-reviewed journals.
A final statement about Mental Health in Science
It is found recurrently on the internet news and comments about another scientific researcher, in particular junior and medior researchers, going through some kind of mental health difficulties in particular those associated with sensory overload events. Promoted by abusive usage of wireless devices, assembled or modified to actuate at a distance and invisible, causing harm to a victim’s neurobiology. This is an old area of knowledge, that now needs to be openly discussed and commented, in a peaceful constructive way towards identification of solutions to all abusive usages of wireless frequency waves (radiation or vibration) in electronic devices. Open solutions are the correct path toward safety in usage of all things related to such technologies. Be knowledgeable to stay safe.
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