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- W4311925313 abstract "The carbon fiber reinforced polymer (CFRP) has been proven to be a cost-effective, efficient, and reliable method for structural rehabilitation or reinforcement. Debonding detection is an important measure to ensure the integrity and performance of such repairs. In this paper, a method of using percussion and unsupervised machine learning to detect the debonding of CFRP plate repaired steel structure is proposed. A steel beam with bonded CFRP and known bonding defects is used as a test specimen. Then, different locations with different bonding conditions on the beam are tapped to generate the percussion sounds, which are recorded by an iPhone. The mel-frequency cepstral coefficient (MFCC) algorithm is employed to extract features from percussion sounds. The unsupervised machine learning algorithm, Gaussian mixture model (GMM) clustering, is implemented for debonding detection. The proposed method achieves 95.8% accuracy in debonding detection on the test specimen." @default.
- W4311925313 created "2023-01-02" @default.
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- W4311925313 date "2022-11-18" @default.
- W4311925313 modified "2023-09-29" @default.
- W4311925313 title "Debonding Detection in Carbon Fiber Reinforced Polymer Plate Repaired Steel Beam Using Percussion and Gaussian Mixture Model Clustering" @default.
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- W4311925313 doi "https://doi.org/10.1109/iccsi55536.2022.9970670" @default.
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