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

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.

A Novel CNN-LSTM Hybrid Model for Prediction of Electro-Mechanical Impedance Signal Based Bond Strength Monitoring.

The recent application of deep learning for structural health monitoring systems for damage detection has potential for improvised structure performance and maintenance for long term durability, and reliable strength. Advancements in electro-mechanical impedance (EMI) techniques have sparked attention among researchers to develop novel monitoring techniques for structural monitoring and evaluation. This study aims to determine the performance of EMI techniques using a piezo sensor to monitor the development of bond strength in reinforced concrete through a pull-out test. The concrete cylindrical samples with embedded steel bars were prepared, cured for 28 days, and a pull-out test was performed to measure the interfacial bond between them. The piezo coupled signatures were obtained for the PZT patch bonded to the steel bar. The damage qualification is performed through the statistical indices, i.e., root-mean-square deviation (RMSD) and correlation coefficient deviation metric (CCDM), were obtained for different displacements recorded for axial pull. Furthermore, this study utilizes a novel Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-based hybrid model, an effective regression model to predict the EMI signatures. These results emphasize the efficiency and potential application of the deep learning-based hybrid model in predicting EMI-based structural signatures. The findings of this study have several implications for structural health diagnosis using a deep learning-based model for monitoring and conservation of building heritage.