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Advances in Concrete Construction Volume 9, Number 4, April 2020 , pages 337-343 DOI: https://doi.org/10.12989/acc.2020.9.4.337 |
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Bayesian demand model based seismic vulnerability assessment of a concrete girder bridge |
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M. Bayat, M. Kia, V. Soltangharaei, H.R. Ahmadi and P. Ziehl
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| Abstract | ||
| In the present study, by employing fragility analysis, the seismic vulnerability of a concrete girder bridge, one of the most common existing structural bridge systems, has been performed. To this end, drift demand model as a fundamental ingredient of any probabilistic decision-making analyses is initially developed in terms of the two most common intensity measures, i.e., PGA and Sa (T1). Developing a probabilistic demand model requires a reliable database that is established in this paper by performing incremental dynamic analysis (IDA) under a set of 20 ground motion records. Next, by employing Bayesian statistical inference drift demand models are developed based on pre-collapse data obtained from IDA. Then, the accuracy and reasonability of the developed models are investigated by plotting diagnosis graphs. This graphical analysis demonstrates probabilistic demand model developed in terms of PGA is more reliable. Afterward, fragility curves according to PGA based-demand model are developed. | ||
| Key Words | ||
| Bayesian Interface; Finite Element Modeling (FEM); Fragility Function Methodology; Probabilistic Seismic Demand Analysis (PSDA) | ||
| Address | ||
| M. Bayat: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, USA M. Kia: Department of Civil Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran V. Soltangharaei: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, USA H.R. Ahmadi: Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh P.O. Box 55136-553, Iran P. Ziehl: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, USA | ||
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