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Seismic demand model class uncertainty in seismic loss analysis for a code-designed URM infilled RC frame building

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Abstract

Probabilistic seismic risk is affected by several sources of uncertainty. Investigating their influence on loss analysis results is essential to obtain reliable quantitative estimations of seismic performance. Within the framework developed by the Pacific Earthquake Engineering Research (PEER) Center for probabilistic seismic loss analysis, this study incorporates the effect of seismic demand model class uncertainty on seismic risk performance metric estimation. The extended formulation is illustrated with an application example code-designed reinforced concrete moment resisting frame building with unreinforced masonry (URM) infill walls, where model class uncertainty related to URM infill walls modeling is propagated to the estimation of seismic financial losses. Model class uncertainty accounts for the variability arising from the use of different modeling solutions, such as the ones associated with the adoption of three equivalent strut macro-models and their modeling parameters. Probabilistic distributions are assigned to selected infill strut model parameters, and a large set of finite element models (FEMs) are generated for each infill strut model class by sampling the model parameter distributions through Latin Hypercube Sampling (LHS). Expected values of repair cost and life-cycle annualized loss are evaluated and compared for two sets of three building performance models. The first set considers the original (median) values of infill strut backbone parameters, while the second set includes model parameter uncertainty. The uncertainty propagation from structural response results to repair costs is also reported for the six performance models. Finally, the contribution of different structural and non-structural element categories to the financial losses is presented.

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Acknowledgements

The first, the third, and the last author wish to acknowledge the financial support received by the Italian Department of Civil Protection (ReLUIS Grant—Reinforced Concrete Structures). Support from Oregon State University to the first author during the time he spent at Oregon State University when developing the work presented here is also acknowledged. The second and fifth authors would like to acknowledge that part of the funding for this study was provided as part of the cooperative agreement 70NANB15H044 between the National Institute of Standards and Technology (NIST) and Colorado State University through a subaward to Oregon State University. The content expressed in this paper are the views of the authors and do not necessarily represent the opinions or views of NIST or the US Department of Commerce. Authors also acknowledge the support received by the Haselton Baker Risk Group for helping to perform the seismic performance analysis through the SP3 web-tool educational license acquired through Oregon State University.

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Authors and Affiliations

  1. Niccolò Cusano University, Via Don Carlo Gnocchi 3, Rome, Italy

    Fabio Romano, Maria Zucconi & Barbara Ferracuti

  2. School of Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR, 97331, USA

    Mohammad S. Alam & Andre R. Barbosa

  3. Department of Structural and Geotechnical Engineering, Sapienza University of Rome, Via Eudossiana 18, Rome, Italy

    Marco Faggella

  4. CONSTRUCT-LESE, Faculty of Engineering (FEUP), University of Porto, 4200-465, Porto, Portugal

    Marco Faggella

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  1. Fabio Romano
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Appendix

Appendix

This appendix reports in detail the 245 unique GM records used for the three numerical models and the different HLs introduced in Sect. 3.1. In particular, Table 4 associates a number to each selected HL and reports the spectral accelerations at the conditioning period for the three building models, \({S}_{a}({T}_{1})\) at each HL.

Table 4 Numbering of the selected HLs and spectral acceleration at the conditioning period, \({S}_{a}({T}_{1})\), for the three numerical models
Full size table

The HLs numbering in Table 4 is introduced for clear correspondence of the HLs in Table 5, shown here below. This table lists the 245 unique GMs, in particular each line reports the record file name together with the HLs and numerical models for which that GM is selected. The record scale factors vary from 0.26 to 4 and are not reported in the table for brevity.

Table 5 Selected 245 unique GM records, reporting also the associated HLs and numerical models
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Romano, F., Alam, M.S., Zucconi, M. et al. Seismic demand model class uncertainty in seismic loss analysis for a code-designed URM infilled RC frame building. Bull Earthquake Eng 19, 429–462 (2021). https://doi.org/10.1007/s10518-020-00994-x

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