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Prediction of Damage Severity in Concrete Structures Using Multivariate Empirical Mode Decomposition of Acoustic Emission Responses
Civil structures may experience various levels of damage during their life cycle due to exposure to different load conditions. Damage severity prediction in structural elements is a critical task to provide optimal maintenance in a timely manner that leads to reduced maintenance costs and avoids any catastrophic failure that may cause serious loss and danger to the owners and end-users. The acoustic Emission (AE) technique is one of the powerful non-destructive testing (NDT) methodologies that can be employed to detect and predict the severity of damage in different systems. Multivariate empirical mode decomposition (MEMD) is the extended version of EMD that can analyze multichannel signals collected from different sensors. In this paper, the damage severity prediction approach based on the combination of MEMD and the Gaussian mixture model (GMM) model is proposed. Unlike existing methods that rely on a single AE measurement, MEMD is used to extract the key AE components that belong to cracks using AE data collected from multiple sensors. The statistical features (e.g., mean, root mean square, kurtosis, skewness, and crest factor) of AE components obtained from MEMD are subsequently estimated. The selected features are then used in the GMM model to predict different severity damages (e.g., minor, and severe levels of damage). Numerical and experimental studies are conducted to validate the performance of the proposed method using a sine example and a large-scale reinforced concrete (RC) beam, respectively. The results show that the proposed approach can accurately predict the severity of damage in the structural elements.
Prediction of Damage Severity in Concrete Structures Using Multivariate Empirical Mode Decomposition of Acoustic Emission Responses
Civil structures may experience various levels of damage during their life cycle due to exposure to different load conditions. Damage severity prediction in structural elements is a critical task to provide optimal maintenance in a timely manner that leads to reduced maintenance costs and avoids any catastrophic failure that may cause serious loss and danger to the owners and end-users. The acoustic Emission (AE) technique is one of the powerful non-destructive testing (NDT) methodologies that can be employed to detect and predict the severity of damage in different systems. Multivariate empirical mode decomposition (MEMD) is the extended version of EMD that can analyze multichannel signals collected from different sensors. In this paper, the damage severity prediction approach based on the combination of MEMD and the Gaussian mixture model (GMM) model is proposed. Unlike existing methods that rely on a single AE measurement, MEMD is used to extract the key AE components that belong to cracks using AE data collected from multiple sensors. The statistical features (e.g., mean, root mean square, kurtosis, skewness, and crest factor) of AE components obtained from MEMD are subsequently estimated. The selected features are then used in the GMM model to predict different severity damages (e.g., minor, and severe levels of damage). Numerical and experimental studies are conducted to validate the performance of the proposed method using a sine example and a large-scale reinforced concrete (RC) beam, respectively. The results show that the proposed approach can accurately predict the severity of damage in the structural elements.
Prediction of Damage Severity in Concrete Structures Using Multivariate Empirical Mode Decomposition of Acoustic Emission Responses
Lecture Notes in Civil Engineering
Desjardins, Serge (editor) / Poitras, Gérard J. (editor) / El Damatty, Ashraf (editor) / Elshaer, Ahmed (editor) / Barbosh, Mohamed (author) / Sadhu, Ayan (author) / Barboush, Murad (author)
Canadian Society of Civil Engineering Annual Conference ; 2023 ; Moncton, NB, Canada
2024-12-13
12 pages
Article/Chapter (Book)
Electronic Resource
English
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