Abstract
As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure.
Highlights
High-pile wharfs need to be monitored in real time to improve their operational performance, prolong their expected life spans, and prevent sudden failures
The primary aim of this research is to establish a novel method for the vibration-based structural damage detection (SDD) of high-pile wharf foundations using finite element modelling (FEM) and 1D Convolutional neural networks (CNNs), which realizes the accurate identification of the existence and location of structural damage
The results indicated the superior performance of the 1D CNNs to extract the damage-sensitive features directly from the raw displacement response data
Summary
High-pile wharfs need to be monitored in real time to improve their operational performance, prolong their expected life spans, and prevent sudden failures. Conventional structural damage detection (SDD) methods for the pile foundations of wharfs are laborious and costly[1,2,3]. Efficiently detecting and precisely locating the structural damage in foundations have always been formidable challenges. Studies have shown that vibration-based SDD methods have been adopted by most structural health monitoring (SHM) systems[3,4,5,6,7]. A large number of the samples in the field of SDD are time series, such as vibration and strain history. 1D CNNs have become a state-of-the-art technique to address vibration-based SDD issues in civil structures
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