Digital Twin Construction for Structural Damage Identification

doi:10.3901/JME.260129

Abstract

Abstract: Damage is the main form of structural failure, and how to quickly and accurately identify damage can effectively avoid failure and fault caused by damages, which is one of the key issues in structural health monitoring. In this study, to improve the accuracy of structural damage identification and achieve rapid damage identification, a digital twin(DT) construction method for structural damage identification is proposed by integrating mode information from mechanism model with sensor measurement data. First, taking the simplified cantilever beam with variable cross-section as an example, an DT mechanism model is built based on structural geometric dimensions, material properties, and so on. The natural frequencies of the health structure are quickly obtained using the Euler-Bernoulli theory. Subsequently, the sensor sampling frequency is set based on the calculated natural frequencies, and the monitored data is decomposed, filtered, and transformed to extract damage features. The most sensitive crack parameter type to damage features is obtained, and the crack length and position are identified by combining the damage features. Then, the feasibility of the mechanism model, damage feature extraction, and damage prediction model is verified through numerical cases. The results illustrate that the proposed method can effectively improve the accuracy of crack identification and quickly identify crack location. Finally, an DT is constructed based on sensor data to identify structural damage in the digital space, which further demonstrates the effectiveness of the proposed digital model for structural damage identification. This study not only provides new method and solution for structural damage identification, but also offers a new reference and guidance for predictive maintenance based on DTs.

Key words: digital twin, damage identification, structure damage, structural health monitoring, predictive maintenance

CLC Number:

HT17

YANG Liangliang, GONG Zhuangzhuang, HE Xiwang, WANG Muchen, MIN Qiang, KAN Ziyun, SONG Xueguan. Digital Twin Construction for Structural Damage Identification[J]. Journal of Mechanical Engineering, 2026, 62(4): 328-341.

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