Probabilistic damage identification of a designed 9-story building using modal data in the presence of modeling errors. (15th January 2017)

• Record Type: Journal Article
• Authors: Behmanesh, Iman; Moaveni, Babak; Papadimitriou, Costas
• Is Part Of: Engineering structures. Volume 131(2017:Jan. 15)
• Page Start: 542
• Page End: 552
• Publication Date: 2017-01-15
• Subjects: Probabilistic damage identification -- Modeling error -- Bayesian FE model updating -- Bayesian model class selection; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2016.10.033
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Effects of modeling errors on damage identification (ID) results are studied. Models validity within the frequency domain is non-uniform. A likelihood function is proposed for damage ID without calibrating a reference model. Optimal subset of modes are selected though a Bayesian model class selection approach. Bayesian model averaging technique is used to account for different weight factors. Abstract: Validity and accuracy of model based identification techniques such as linear finite element (FE) model updating are sensitive to modeling errors. Models used for the design and performance assessment of civil structures often contain large modeling errors for certain frequency ranges of response. In other words, modeling errors have unequal effects on different vibration modes of structures. Therefore, the performance of FE model updating for damage identification is sensitive to the type and the subset of data used and to the residual weight factors. This study proposes a process to mitigate the effects of modeling errors by selecting the optimal subset of modes and the optimal modal residual weights. Multiple model updating classes are defined based on different subsets of modes and different weight factors. Structural damage is then identified using Bayesian model class selection and model averaging techniques over the results of all the considered model updating classes. In addition, a new likelihood function is defined to allow damage identification without the need for calibrating a reference FE model. Performance of the proposed damage identification process and the new likelihood function is evaluated numerically at multiple levels of modeling errors and structural damage on the SAC 9-story steel moment frame. It is shown that the structural damages can be identified with negligible bias when the proposed likelihood and updating process is implemented.