The use of simple models for response prediction of building structures is preferred in earthquake engineering for risk evaluations at regional scales, as they make computational studies more feasible. The primary impediment in their gainful use presently is the lack of viable methods for quantifying (and reducing upon) the modeling errors/uncertainties they bear. This study presents a Bayesian calibration method wherein the modeling error is embedded into the parameters of the model. Here, the method is specifically described for coupled shear-flexural beam models here, but it can be applied to any parametric surrogate model. The major benefit the method offers is the ability to consider the modeling uncertainty in the forward prediction of any degree-of-freedom or composite response regardless of the data used in calibration. The method is extensively verified using two synthetic examples. In the first example, the beam model is calibrated to represent a similar beam model but with enforced modeling errors. In the second example, the beam model is used to represent the detailed finite element model of a 52-story building. Both examples show the capability of the proposed solution to provide realistic uncertainty estimation around the mean prediction.
Ghahari, S. Farid, et al. "Quantifying modeling uncertainty in simplified beam models for building response prediction." Structural Control and Health Monitoring, vol. 29, no. 11, Aug. 2022. https://doi.org/10.1002/stc.3078
Ghahari, S. Farid, Sargsyan, Khachik, Çelebi, Mehmet, & Taciroglu, Ertugrul (2022). Quantifying modeling uncertainty in simplified beam models for building response prediction. Structural Control and Health Monitoring, 29(11). https://doi.org/10.1002/stc.3078
Ghahari, S. Farid, Sargsyan, Khachik, Çelebi, Mehmet, et al., "Quantifying modeling uncertainty in simplified beam models for building response prediction," Structural Control and Health Monitoring 29, no. 11 (2022), https://doi.org/10.1002/stc.3078
@article{osti_1882634,
author = {Ghahari, S. Farid and Sargsyan, Khachik and Çelebi, Mehmet and Taciroglu, Ertugrul},
title = {Quantifying modeling uncertainty in simplified beam models for building response prediction},
annote = {The use of simple models for response prediction of building structures is preferred in earthquake engineering for risk evaluations at regional scales, as they make computational studies more feasible. The primary impediment in their gainful use presently is the lack of viable methods for quantifying (and reducing upon) the modeling errors/uncertainties they bear. This study presents a Bayesian calibration method wherein the modeling error is embedded into the parameters of the model. Here, the method is specifically described for coupled shear-flexural beam models here, but it can be applied to any parametric surrogate model. The major benefit the method offers is the ability to consider the modeling uncertainty in the forward prediction of any degree-of-freedom or composite response regardless of the data used in calibration. The method is extensively verified using two synthetic examples. In the first example, the beam model is calibrated to represent a similar beam model but with enforced modeling errors. In the second example, the beam model is used to represent the detailed finite element model of a 52-story building. Both examples show the capability of the proposed solution to provide realistic uncertainty estimation around the mean prediction.},
doi = {10.1002/stc.3078},
url = {https://www.osti.gov/biblio/1882634},
journal = {Structural Control and Health Monitoring},
issn = {ISSN 1545-2255},
number = {11},
volume = {29},
place = {United States},
publisher = {Wiley},
year = {2022},
month = {08}}
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; USGS; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
Grant/Contract Number:
NA0003525
OSTI ID:
1882634
Report Number(s):
SAND2022-10827J; 708983
Journal Information:
Structural Control and Health Monitoring, Journal Name: Structural Control and Health Monitoring Journal Issue: 11 Vol. 29; ISSN 1545-2255
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 374, Issue 2060https://doi.org/10.1098/rsta.2015.0032