Modal identification of civil structures via covariance-driven stochastic subspace method

Area of Science:

• Civil Engineering
• Structural Dynamics
• System Identification

Background:

• Modal parameter identification is crucial for civil structure dynamic analysis and vibration control.
• Civil engineering applications often involve unknown external forces, unlike mechanical systems.
• Output-only data records are commonly available for existing structures.

Purpose of the Study:

• To introduce the covariance-driven stochastic subspace identification (SSI) method for modal identification.
• To demonstrate the accuracy and efficiency of the SSI method through a case study.
• To highlight the impact of noise on identification results and accurate mode order determination.

Main Methods:

• The study details the covariance-driven stochastic subspace identification (SSI) method.
• It utilizes system identification theory, linear algebra (e.g., Singular Value Decomposition), and statistics.
• Matrix calculations are employed to identify the system matrix and subsequently modal parameters.

Main Results:

• The SSI method successfully identifies natural frequencies, modal shapes, and damping ratios for multiple modes simultaneously.
• A case study validates the accuracy and efficiency of the SSI method compared to an alternative approach.
• The influence of noise on output signals and strategies for accurate mode order determination are emphasized.

Conclusions:

• The covariance-driven SSI method is an efficient and accurate technique for modal identification of civil structures using output-only data.
• The method's robustness to noise and accurate mode order determination are critical for reliable dynamic analysis.
• SSI offers a valuable tool for understanding and controlling the dynamic behavior of civil infrastructure.