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Parameters identification of cable stayed footbridges using Bayesian inference

  • Stochastics and Probability in Engineering Mechanics
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Abstract

Numerical modeling of actual structural systems is a very complex task mainly due to the lack of complete knowledge on the involved parameters. Simplified assumptions on the uncertain geometry, material properties and boundary conditions make the numerical model response differ from the actual structural response. Improvements of the finite element (FE) models to obtain accurate response predictions can be achieved by vibration based FE model updating which uses experimental measures to minimize the differences between the numerical and experimental modal features (i.e. natural frequencies and mode shapes). Within this context, probabilistic model updating procedures based on the Bayes’ theorem were recently proposed in the literature in order to take into account the uncertainties affecting the structural parameters and their influence on the structural response. In this paper, a novel framework to efficiently estimate the posterior marginal PDF of the selected model parameters is proposed. First, the main dynamic parameters to be used for model updating are identified by ambient vibration tests on an actual structural system. Second, a first numerical FE model is developed to perform initial sensitivity analysis. Third, a surrogate model based on polynomial chaos is calibrated on the initial FE model to significantly reduce computational costs. Finally, the posterior marginal PDFs of the chosen model parameters are estimated. The effectiveness of the proposed method is demonstrated using a FE numerical model describing a curved cable-stayed footbridge located in Terni (Umbria Region, Central Italy).

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Authors and Affiliations

  1. CRIACIV/Department of Civil and Environmental Engineering, University of Perugia, via G. Duranti 93, 06125, Perugia, Italy

    Chiara Pepi & Massimiliano Gioffre’

  2. Department of Civil and Environmental Engineering, Cornell University, 220 Hollister Hall, Ithaca, NY, 14853, USA

    Mircea D. Grigoriu

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  1. Chiara Pepi
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  2. Massimiliano Gioffre’
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  3. Mircea D. Grigoriu
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Correspondence to Chiara Pepi.

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Pepi, C., Gioffre’, M. & Grigoriu, M.D. Parameters identification of cable stayed footbridges using Bayesian inference. Meccanica 54, 1403–1419 (2019). https://doi.org/10.1007/s11012-019-01019-x

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