Operational modal analysis of a scaled bridge model using EFDD and SSI methods

ALTUNIŞIK A. C., Bayraktar A., SEVİM B.

INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES, vol.19, no.5, pp.320-330, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 5
  • Publication Date: 2012
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.320-330
  • Karadeniz Technical University Affiliated: Yes


In this study, dynamic characteristics of a laboratory bridge model are determined by operational modal analysis using frequency and time domain methods. For this purpose, a reinforced box girder concrete bridge model is constructed in laboratory conditions. The bridge deck consists of a main span of 3 m and two side span of 1.5 m each. The structural system of the model bridge consists of deck, piers and foundation. The total length of bridge deck is 6 m and width of bridge deck is 60 cm. Ambient vibration tests are conducted to the model bridge to identify its natural frequencies, mode shapes and damping ratios. Natural excitations such as wind and impact hammer are used to vibrate the model bridge. Vibration data is gathered from bridge deck. Measurement time, frequency span and effective mode number are determined by Consider similar studies in literature. Sensitivity accelerometers are placed to collect signals from the measurements. The signals collected from the tests are processed by operational modal analysis; and the dynamic characteristics of the bridge model are estimated using enhanced frequency domain decomposition (EFDD) method in the frequency domain and stochastic subspace identification (SSI) method in the time domain. The dynamic characteristics obtained from both methods are found to be close to each other. Maximum 2.68% differences are obtained between natural frequencies for the first mode. Modal assurance criteria values are between 0.85-1.00. This shows that EFDD and SSI results are almost overlapped. It can be concluded that the both of enhanced frequency domain decomposition and stochastic subspace identification methods are very useful to identify the dynamic characteristics of the bridge model.