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- W4313292711 abstract "Recently, structural damage identification has become an important research direction in civil engineering. Different innovative approaches have been conducted to improve the accuracy of the damage identification method. Among them, the autoregressive (AR)-based method has standout to be one of the most popular techniques for detecting structural damage due to its non-destructive nature and the ability to localize damage accurately based on trial-and-error practice. In this work, a novel hybrid method combining AR models and Artificial Neural Networks (ANNs) has been developed to solve damage identification problems of structure. The main idea is to use the coefficients of the AR models as inputs for the ANNs processing system. The proposed method is applied to the famous vibration data of the Z24 Bridge for validation. The highly accurate results have proven the effective performance of the proposed method in detecting damage of the structure." @default.
- W4313292711 created "2023-01-06" @default.
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- W4313292711 date "2022-12-17" @default.
- W4313292711 modified "2023-10-18" @default.
- W4313292711 title "The Application of a Hybrid Autoregressive and Artificial Neural Networks to Structural Damage Detection in Z24 Bridge" @default.
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- W4313292711 doi "https://doi.org/10.1007/978-981-19-4835-0_36" @default.
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