Abstract
Subspace system identification is a class of methods to estimate state-space model based on low rank characteristic of a system. State-space-based subspace system identification is the dominant subspace method for system identification in health monitoring of the civil structures. The weight matrices of canonical variate analysis (CVA), principle component (PC), and unweighted principle component (UPC), are used in stochastic subspace identification (SSI) to reduce the complexity and optimize the prediction in identification process. However, researches on evaluation and comparison of weight matrices’ performance are very limited. This study provides a detailed analysis on the effect of different weight matrices on robustness, accuracy, and computation efficiency. Two case studies including a lumped mass system and the response dataset of the Alamosa Canyon Bridge are used in this study. The results demonstrated that UPC algorithm had better performance compared to two other algorithms. It can be concluded that though dimensionality reduction in PC and CVA lingered the computation time, it has yielded an improved modal identification in PC.
Highlights
Design of engineering structures is carried out in a way to ensure safety of occupants during their service life while reducing cost and at the same time maintaining quality
Scattering of the poles is lowest in principle component (PC) for both experimental and numerical data whereas canonical variate analysis (CVA) has the smallest variances among all algorithms
The process of dimensionality reduction has reduced the time efficiency in PC and CVA but has an enhanced estimation results are achieved in PC
Summary
Design of engineering structures is carried out in a way to ensure safety of occupants during their service life while reducing cost and at the same time maintaining quality. Failure to do so may lead to catastrophic human losses and necessitate heavy capital expenditure or expensive maintenance repair. It is important to monitor changes in the structural parameters such as stiffness, damping, and mass. Structural health monitoring (SHM) is an emerging technology to detect damage in structure and to identify potential deficiency states at an early stage by examining sensors data mounted on structures or other sensing devices [1]. Vibration-based damage detection (VDD) is the dominant technique for a robust damage detection that uses the changes in dynamic parameters of structures for finding indications of damages [2,3].
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