Damage detection in concrete structures with impedance data and machine learning

• Autorzy: Anjum Asraar , Hrairi Meftah , Aabid Abdul , Yatim Norfazrina , Ali Maisarah

Identyfikatory

DOI

10.24425/bpasts.2024.149178

• Języki publikacji: EN

Abstrakty

EN

This study aims to evaluate the effectiveness of machine learning (ML) models in predicting concrete damage using electromechanical impedance (EMI) data. From numerous experimental evidence, the damaged mortar sample with surface-mounted piezoelectric (PZT) material connected to the EMI response was assessed. This work involved the different ML models to identify the accurate model for concrete damage detection using EMI data. Each model was evaluated with evaluation metrics with the prediction/true class and each class was classified into three levels for testing and trained data. Experimental findings indicate that as damage to the structure increases, the responsiveness of PZT decreases. Therefore, we examined the ability of ML models trained on existing experimental data to predict concrete damage using the EMI data. The current work successfully identified the approximately close ML models for predicting damage detection in mortar samples. The proposed ML models not only streamline the identification of key input parameters with models but also offer cost-saving benefits by reducing the need for multiple trials in experiments. Lastly, the results demonstrate the capability of the model to produce precise predictions.

Słowa kluczowe

EN

concrete structures; damage; piezoelectric materials; electromechanical impedance; machine learning

PL

konstrukcja betonowa; szkoda; materiał piezoelektryczny; impedancja elektromechaniczna; uczenie maszynowe

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 3
• Strony: art. no. e149178
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Anjum Asraar

• Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia

autor

Hrairi Meftah

• Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia

• https://orcid.org/0000-0003-3598-8795

autor

Aabid Abdul

• Department of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh 11586, Saudi Arabia

• https://orcid.org/0000-0002-4355-9803

autor

Yatim Norfazrina

• Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia

• https://orcid.org/0000-0002-9355-0102

autor

Ali Maisarah

• Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).