Abstract
A new likelihood function is proposed for probabilistic damage identification of civil structures that are usually modeled with many simplifying assumptions and idealizations. Data from undamaged and damaged states of the structure are used in the likelihood function and damage is identified through a Bayesian finite element (FE) model updating process. The new likelihood function does not require calibration of an initial FE model to a baseline/reference model and is based on the difference between damaged and healthy state data. It is shown that the proposed likelihood function can identify structural damage as accurately as two other types of likelihood functions frequently used in the literature. The proposed likelihood is reasonably accurate in the presence of modeling error, measurement noise and data incompleteness (number of modes and number of sensors). The performance of FE model updating for damage identification using the proposed likelihood is evaluated numerically at multiple levels of modeling errors and structural damage. The effects of modeling errors are simulated by generating identified modal parameters from a model that is different from the FE model used in the updating process. It is observed that the accuracy of damage identifications can be improved by using the identified modes that are less affected by modeling errors and by assigning optimum weights between the eigen-frequency and mode shape errors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rytter A (1993) Vibration based inspection of civil engineering structures. Ph.D. dissertation, Department of Building and Technology and Structural Engineering of Aalborg University, Denmark
Sohn H, Farrar CR, Hemez FM, Shunk DD, Stinemates DW, Nadler BR (2003) A review on structural health monitoring literature: 1996–2001. Technical report annex to SAMCO summer academy, Los Alamos National Laboratory, Cambridge
Doebling SW, Farrar CR, Prime MB, Shevitz DW (1996) Damage identification and health monitoring of structural and mechanical systems for changes in their vibration characteristics. Technical report LA-13070-MS, Los Alamos National Laboratory, Cambridge
Carden EP, Fanning P (2004) Damage detection and health monitoring of large space structures. Struct Health Monit 3:355–377
Farhat C, Hemez FM (1993) Updating finite element dynamic models using element by element sensitivity methodology. AIAA J 31(9):1702–1711
Friswell MI, Mottershead JE (1995) FE model updating in structural dynamics. Kluwer Academic, Boston
Beck JL, Katafygiotis LS (1998) Updating models and their uncertainties. I: Bayesian statistical framework. ASCE J Eng Mech 124(4):455–461
Sanayei M, McClain JAS, Wadia-Fascetti S, Santini EM (1999) Parameter estimation incorporating modal data and boundary condition. J Struct Eng 125(9):1048–1055
Mottershad JE, Link M, Friswell MI (2011) The sensitivity method in finite element model updating: a tutorial. Mech Syst Signal Process 25(7):2275–2296
Teughles A, De Roeck G (2004) Structural damage identification of the highway bridge Z24 by FE model updating. J Sound Vib 278(3):589–610
Huth O, Feltrin G, Maeck J, Kilic N, Motavalli M (2005) Damage identification using modal data: experiences on prestressed concrete bridge. J Struct Eng 131(12):1898–1910
Reynders E, De Roeck D, Bakir PG, Sauvage C (2007) Damage identification on the Tilff Bridge by vibration monitoring using optical fiber strain sensors. J Eng Mech 133(2):185–193
Moaveni B, He X, Conte JP, Restrepo JI (2010) Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table. Struct Saf 32(5):347–356
Moaveni B, Behmanesh I (2012) Effects of changing ambient temperature on finite element model updating of the Dowling Hall Footbridge. Eng Struct 43:58–68
Behmanesh I, Moaveni B (2013) Probabilistic damage identification of the Dowling Hall Footbridge using Bayesian FE model updating. In: Proceedings of 31st International Modal Analysis Conference (IMAC-XXXI), Garden Grove, CA
SAC (2000) State of the art report on systems performance of steel moment frames subject to earthquake ground shaking. FEMA 355C report, Washington, DC
Beck JL, Au SK, Vanik MW (2001) Monitoring structural health using a probabilistic measure. Comput-Aided Civ Inf Eng 16:1–11
Yuen KV, Beck JL, Au SK (2004) Structural damage detection and assessment by adaptive Markov chain Monte Carlo simulation. Struct Contr Health Monit 11:327–347
Ching J, Beck JL (2004) New Bayesian model updating algorithm applied to a structural health monitoring benchmark. Struct Health Monit 3:313–332
Mthembu L, Marwala T, Friswell ML, Adhikari S (2011) Model selection in finite element model updating using the Bayesian evidence statistics. Mech Syst Signal Process 25:2399–2412
Goller B, Beck JL, Schueller GI (2012) Evidence-based identification of weighting factors in Bayesian model updating using modal data. J Eng Mech 138:430–440
Haralampidis Y, Papadimitriou C, Pavlidou, M (2005) Multi-objective framework for structural model identification. Earthquake Engineering and Structural Dynamics, 34:665–685.
Acknowledgements
The authors would like to acknowledge the support of this study by the National Science Foundation Grant No. 1125624 which was awarded under the Broadening Participation Research Initiation Grants in Engineering (BRIGE) program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Behmanesh, I., Moaveni, B. (2014). Bayesian FE Model Updating in the Presence of Modeling Errors. In: Atamturktur, H., Moaveni, B., Papadimitriou, C., Schoenherr, T. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-04552-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-04552-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04551-1
Online ISBN: 978-3-319-04552-8
eBook Packages: EngineeringEngineering (R0)