Machine learning approach for predicting impedance signatures of construction steel structures in various tensile pull actions

Parida, Lukesh; Moharana, Sumedha; Giri, Sourav Kumar

doi:10.1016/j.matpr.2023.03.741

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Cited by 4 publications

References 22 publications

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Application of Deep Learning Approach for Predicting Electromechanical Impedance Signal of Steel-Concrete Bond Failure: Baseline Free Approach

Parida,

Moharana

2024

Lecture Notes in Civil Engineering

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Application of Deep Learning Approach for Predicting Electromechanical Impedance Signal of Steel-Concrete Bond Failure: Baseline Free Approach

Parida,

Moharana

2024

Lecture Notes in Civil Engineering

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Early detection of thermal instability in railway tracks using piezo-coupled structural signatures

Banerjee,

Moharana,

Parida

2023

Journal of Infrastructure Intelligence and Resilience

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A proof of concept study on reliability assessment of different metal foil length based piezoelectric sensor for electromechanical impedance techniques

Parida,

Moharana,

Vicente

et al. 2024

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Lead zirconate titanate (PZT) patches gained popularity in structural health monitoring (SHM) for its sensing and cost effective. However, a robust installation of PZT patches is challenging due to the often-complex geometry and non-accessibility of structural parts. For tubular structures, the curved surface can compromise the perfect bonding of PZT patches. To alleviate the above-mentioned challenges, the non-bonded and reusable configuration of sensor received considerable interest in the field of SHM. However, ensuring the repeatability and reproducibility of Electro-Mechanical Impedance (EMI) measurements is crucial to establish the reliability of these techniques. This work investigated the repeatability and reproducibility measures for one of non-bonded configuration of PZT patch i.e., Metal Foil Based Piezo Sensor (MFBPS). In addition, the concept, application, and suitability of MFBPS for impedance-based monitoring technique of Civil infrastructure are critically discussed. This study evaluates the effect of length of MFBPS on piezo coupled admittance signature. Also, this study evaluates repeatability and reproducibility of EMI measurements via statistical tools such as ANOVA and Gage R&R analysis. The statistical index CCDM was used to quantify the deviations of impedance signals. The overall result shows that the repeatability of the EMI measurements improves with a metal foil length of 500 mm. Overall, this investigation offers a useful point of reference for professionals and scholars to ensure the reliability of MFBPS for EMI techniques, a variant of piezoelectric sensor for SHM applications.

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Machine learning approach for predicting impedance signatures of construction steel structures in various tensile pull actions

Cited by 4 publications

References 22 publications

Application of Deep Learning Approach for Predicting Electromechanical Impedance Signal of Steel-Concrete Bond Failure: Baseline Free Approach

Application of Deep Learning Approach for Predicting Electromechanical Impedance Signal of Steel-Concrete Bond Failure: Baseline Free Approach

Early detection of thermal instability in railway tracks using piezo-coupled structural signatures

A proof of concept study on reliability assessment of different metal foil length based piezoelectric sensor for electromechanical impedance techniques

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