Bone-Implant Osseointegration Monitoring Using Electro-mechanical Impedance Technique and Convolutional Neural Network: A Numerical Study

Truong, Tran-De-Nhat; Pradhan, Ananta Man Singh; Nguyen, Thanh-Truong; Tran, Manh-Hung; Nguyen, Chi-Khai; Ho, Duc-Duy; Huynh, Thanh-Canh

doi:10.1007/s10921-023-01021-0

Cited by 3 publications

(1 citation statement)

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“…Since the EMA responses contain important information about the dynamic properties of the host structure, the structural integrity can be accessed by monitoring changes in the EMA signatures. Thanks to the short wavelengths generated during PZT excitation, this technology allows for the accurate detection of minor and early structural damages [25,26]. Researchers have implemented the technique for the SHM of prestressed structures.…”

Section: Introductionmentioning

confidence: 99%

Structural Condition Assessment of Steel Anchorage Using Convolutional Neural Networks and Admittance Response

Ho,

Kim,

Hoang

et al. 2024

Buildings

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Structural damage in the steel bridge anchorage, if not diagnosed early, could pose a severe risk of structural collapse. Previous studies have mainly focused on diagnosing prestress loss as a specific type of damage. This study is among the first for the automated identification of multiple types of anchorage damage, including strand damage and bearing plate damage, using deep learning combined with the EMA (electromechanical admittance) technique. The proposed approach employs the 1D CNN (one-dimensional convolutional neural network) algorithm to autonomously learn optimal features from the raw EMA data without complex transformations. The proposed approach is validated using the raw EMA response of a steel bridge anchorage specimen, which contains substantial nonlinearities in damage characteristics. A K-fold cross-validation approach is used to secure a rigorous performance evaluation and generalization across different scenarios. The method demonstrates superior performance compared to established 1D CNN models in assessing multiple damage types in the anchorage specimen, offering a potential alternative paradigm for data-driven damage identification in steel bridge anchorages.

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Section: Introductionmentioning

confidence: 99%