Comparison of system identification methods for extracting modal parameters (Case study: 22 Bahman Bridge, Ferdowsi University of Mashhad)

Document Type : Original Article

Authors

• nima kowsari ¹
• zaher rezaie ¹
• mahmud golriz ¹
• Ahmad Shooshtari ²

¹ Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

² Civil Engineering Department,, Ferdowsi University of Mashhad, Mashhad, Iran

10.22067/jfcei.2025.91585.1336

Abstract

System identification plays a crucial role in structural health monitoring. By using damage-sensitive features such as natural frequencies and mode shapes and comparing these features to the healthy state of the system, damage can be identified. This paper investigates the efficiency of system identification methods. To this end, the Stochastic Subspace Identification (SSI) method in the time domain, the Frequency Domain Decomposition (FDD) and Poly Reference Least Squares Complex Frequency domain (PLSCF) methods in the frequency domain, and the Empirical Mode Decomposition (EMD) method in the time-frequency domain were selected for evaluation. To assess the accuracy of these methods, both numerical and experimental data were utilized. The numerical data was obtained from the ASCE-AISC benchmark structure, while the experimental data consisted of vibration data obtained from a modal test performed on the 22 Bahman Bridge at Ferdowsi University of Mashhad. This approach allowed for an evaluation of method performance under controlled conditions (numerical) and in the presence of environmental noise (experimental). The results demonstrate that the FDD and SSI methods exhibited superior accuracy and suitability for estimating the modal characteristics of structures. The PLSCF method proved to be sensitive to environmental noise, leading to errors in the estimated modal parameters. The EMD method, while suitable for processing non-stationary and nonlinear signals, exhibited lower accuracy compared to other methods. These findings provide valuable information for researchers in the field of structural health monitoring in selecting appropriate methods for implementation in real-world applications or in developing further advancements to these techniques.

Keywords

• System identification
• Stochastic Subspace Identification
• Frequency Domain Decomposition
• Poly Reference Least Square Complex Frequency domain
• Empirical Mode Decomposition

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