Ambient Vibration Tests &
Operational Modal Analysis
by Ardalan Sabamehr, Ph.D.
Nowadays, Operational Modal Analysis (OMA) has become very attractive in the field of civil engineering because the tests are cost-effective, fast and do not interfere with operations of the structure. The identified modal parameters are representative of the actual behavior of the structure under its standard conditions with no additional exciter or shaker needed on the structure. The vibrations naturally present in the structure are caused by ambient operational loads which are caused by the wind, nearby traffic, and general human activity. Additionally, this kind of vibration with the low amplitude found in operational conditions requires very sensitive, low-noise sensors and a high-performance measurement chain.
Sensor spread for a typical ambient vibration test (Sensequake)
The goal of OMA is to estimate the dynamic properties of Linear Time-Invariant (LTI) systems purely from records of their dynamic response. Thus, the unknown environmental and operational loads play a fundamental role in testing and in the subsequent modal analysis. While the environmental excitation is advantageous when large civil structures are tested, the data acquisition and, above all, data processing require supplemental attention to carry out successful, output-only modal tests.
There are a number of algorithms for comprehensive modal analyses which work in different domains such as the time domain, frequency domain, and time-frequency domain. In the frequency domain, one of the most common technique is Frequency Domain Decomposition (FDD) which is capable to extract the natural frequency and mode shape of the structure. Enhanced FDD (or EFDD) is a more developed version of the method that can identify a damping ratio in addition to the other parameters. Research shows that in the presence of low Signal Noise ratio (SNR), closed modes cannot be detected easily using frequency domain methods. Thus, the application of a robust technique in the time domain is required to detect all modes with any characteristics. The most common robust method in the time domain is called Stochastic Subspace Identification (SSI). The details of the algorithms will be explained in coming blogs.
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