A hybrid methodology for structural damage detection uniting FEM and 1D-CNNs: Demonstration on typical high-pile wharf. (1st April 2022)

• Record Type: Journal Article
• Authors: Zhou, Yujue; Zheng, Yonglai; Liu, Yongcheng; Pan, Tanbo; Zhou, Yubao
• Is Part Of: Mechanical systems and signal processing. Volume 168(2022)
• Publication Date: 2022-04-01
• Subjects: High-pile wharf -- Pile foundations -- Vibration -- Structural health monitoring -- Structural damage detection -- Finite element modeling -- 1D Convolutional neural networks; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108738
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: This study establishes a method framework based on FEM and DL to realize the identification of overall structural damage of high-pile wharfs. The datasets required for the training of the CNNs can be directly reconstructed by the displacement response of FE model. The comprehensive performance of the CNNs based on the displacement response dataset in component form is significantly better. The results show that the CNN are more sensitive to the longitudinal and lateral displacement responses at the beam-pile nodes. Abstract: Vibration-based structural damage detection (SDD) has been a subject of intense research in structural health monitoring (SHM) for large civil engineering structures over the decades. The performance of the conventional SDD approaches predominantly relies on the rational choices of the damage feature and classifier. Hand-crafted features or fixed classifiers would not be the optimal choice for all structural damaged scenarios. This paper proposes a novel, quick and precise real-time SDD framework for high-pile wharf foundations using a combination of finite element modeling and 1D convolutional neural networks (CNNs). The distinct advantage of this method lies in extracting the damage-related features from the raw displacement response directly and automatically, and the computational complexity of the compact 1D CNNs is significantly lower because the data processing involves only simple 1D operations. The results show that the presented 1D CNNs have a superior ability to accurately identify the occurrence and location of damage in real time. In addition, the comprehensive performance of the CNNs trained by the displacement response dataset in component form is significantly better than that based on the dataset in absolute value form. The results also demonstrated that although the proposed CNNs are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure, the vertical component still has a positive effect on the improvement of the generalization and robustness of the CNNs.