Combined digital twin and hierarchical deep learning approach for intelligent damage identification in cable dome structure. (1st January 2023)

• Record Type: Journal Article
• Authors: Wang, Longxuan; Liu, Hongbo; Chen, Zhihua; Zhang, Fan; Guo, Liulu
• Is Part Of: Engineering structures. Volume 274(2023)
• Publication Date: 2023-01-01
• Subjects: Cable dome -- Deep learning -- Digital twin -- Damage identification; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.115172
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Combining DT and hierarchical DL to realize intelligent identification of structural damage. Study an establishment method of high-fidelity digital twin model based on offline data and online data-driven. Develop a program for automatic establishing damage database of cable dome structure. Propose a DL framework which can identify the damage type, location and degree with high accuracy. The approach can provide a new basis for structural damage identification with broad application prospects. Abstract: Accurate identification of structural damage is the most critical step in structural health monitoring. Traditional damage identification strategies are easily interfered by environmental and human factors, resulting in low-accuracy of identification. In this light, a combined digital twin (DT) and hierarchical deep learning (DL) approach for intelligent damage identification in cable dome structures is proposed in this paper. Based on actual engineering cases, a DT model that accurately maps the physical structure of the cable dome is constructed using APDL based on data. A cable dome structure damage sample database is then automatically established through the large-scale finite element analysis of DT. Finally, the damage features of the data samples are extracted using the hierarchical DL framework proposed in this study. Accuracy verification based on cable force confirms that the established DT model can accurately reflect the mechanical state of the physical structure. The identification results of the trained network on a test set demonstrate that the proposed framework can intelligently identify the damage type, damage location, and damage degree in the cable dome structure with a high accuracy and strong robustness. The proposed intelligent damage identification approach is feasible and reliable and can provide a new basis for structural damage identification with broad application prospects.