CNN Deep Learning-based Monitoring of Stress and Damage Using of Electromechanical Impedance Responses of CSA Sensor

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Abstract: A multi-task 2D CNN model is designed for integrated monitoring stress and damage in concrete specimens utilizing the raw impedance signatures of capsule-like smart aggregated (CSA). The fundamental theory of CSA-based EMI method is presented to describe how the sensor responds to compressive loads. Next, compression tests on a CSA-embedded concrete cylinder are conducted to record the stress-damage EMI responses of CSA sensor under applied stresses. The multi-task 2D CNN model learned the impedance signals for predicting the concrete stress and damage is constructed. Consequently, the generalization and robustness of the developed model are tested against noise and untrained data.

Design, Construction, Maintenance, ISSN / E-ISSN: 2944-912X / 2732-9984, Volume 4, 2024, Art. #24

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Jeong-Tae Kim, Quoc-Bao Ta, Ngoc-Lan Pham, "CNN Deep Learning-based Monitoring of Stress and Damage Using of Electromechanical Impedance Responses of CSA Sensor," Design, Construction, Maintenance, vol. 4, pp. 222-227, 2024, DOI:10.37394/232022.2024.4.24
Jeong-Tae Kim, Quoc-Bao Ta, Ngoc-Lan Pham. CNN Deep Learning-based Monitoring of Stress and Damage Using of Electromechanical Impedance Responses of CSA Sensor. Design, Construction, Maintenance. 2024;4:222-227. 10.37394/232022.2024.4.24
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