A Time–Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen

A Time–Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to...

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Bibliografische gegevens
Hoofdauteurs: Naiwei Lu, Yiru Liu, Jian Cui, Xiangyuan Xiao, Yuan Luo, Mohammad Noori
Formaat: Artikel
Taal:English
Gepubliceerd in: MDPI AG 2024-12-01
Reeks:Sensors
Onderwerpen:
structural damage identification
structural health monitoring
convolutional neural network
gram angle difference field
cable-stayed bridge
Online toegang:https://www.mdpi.com/1424-8220/24/24/8007

Internet

https://www.mdpi.com/1424-8220/24/24/8007

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