A Comparative Study on the Identification of Building Natural Frequencies based on Parametric Models

Tarek Edrees Saaed, George Nikolakopoulos, and Jan-Erik Jonasson


System Identification, Structural Health Monitoring, Modal Identification Techniques, Structural Behaviour


The analysis and design of civil engineering structures is a complex problem, which is based on many assumptions to simplify these operations. This in turn, leads to a difference in the structural behavior between calculations based models and real structures. Structural identification was proposed by many researchers as a tool to reduce this difference between models and actual structures. Moreover, Parametric models and non-parametric models were used intensively for system identification by many researchers. In this research effort, the system identification concept is utilized to identify the natural frequencies for a steel building’s frames. Different black box linear parametric models such as Transfer Function model (TF), Auto-Regressive model with eXternal input model (ARX), Auto-Regressive Moving Average with eXternal input (ARMAX) model, Output Error model structure (OE), and Box-Jenkins model (BJ) were examined for identifying the first 10th natural frequencies for the building’s frames, based on simulation results. Abaqus 6.12 finite-element software was utilized to perform the time history analysis for the examples and the obtained responses at one point of the roofs (assumed as a sensor) were further processed by the parametric models to obtain the building’s natural frequencies based on the Abaqus time history analysis results (assumed as a measurements). After that, Abaqus 6.12 was utlized again to perform another analysis, which is called frequency analysis to obtain the building’s natural frequencies and mode shapes based on the stiffness and mass (not the measurements) of the buildings. The results showed that the linear parametric models TF, ARX, ARMAX, OE, and BJ are robust to identify the natural frequencies of building and they are recommend for future work.

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