Damage identification method of long-span spatial structure based on time-series model of measured data

• Published in: Journal of civil structural health monitoring Vol. 13; no. 2-3; pp. 693 - 707
• Main Authors: Xu, Chenjia, Lei, Honggang, Wang, Guoqing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-03-2023; Springer Nature B.V
• Subjects: Civil Engineering; Control; Damage detection; Discriminant analysis; Dynamical Systems; Engineering; Identification; Identification methods; Mathematical models; Measurement Science and Instrumentation; Original Paper; Parameter identification; Parameter sensitivity; Principal components analysis; Steel structures; Structural damage; Structural health monitoring; Time series; Vibration; Damage identification; Structural health monitoring; Time series model; Spatial structure; Damage sensitive feature
• ISSN: 2190-5452; 2190-5479
• DOI: 10.1007/s13349-023-00672-x

Aiming at the damage misjudgment caused by incomplete modal information and unclear physical meaning of parameters in damage identification of long-span spatial structures, a damage identification method based on time-series analysis model established from measured data of structural health monitoring system is proposed. First, the selection method and modeling process of time-series analysis model based on acceleration response data are illustrated through the theoretical derivation. Subsequently, the damage sensitive feature (DSF) constructed by autoregressive parameters and Mahalanobis distance (MD) discriminant function is proposed for structural damage identification, which is dimension reduced by principal component analysis (PCA). Finally, based on the long-span steel structure in Taiyuan Botanical Garden, the effectiveness of damage identification method is studied by numerical simulation method, and the current damage status of the structure is identified through the measured data. The results show that the proposed method can effectively identify the structural damage, accurately locate the damage location and reflect the damage degree. The proposed DSF is sensitive to minor structural damage while reducing the amount of calculation. It is suitable for damage identification of complex structures and has application value in the analysis of health monitoring systems measured response data in practical engineering.