Quantifying modeling uncertainty in simplified beam models for building response prediction

• Published in: Structural control and health monitoring Vol. 29; no. 11
• Main Authors: Ghahari, S. Farid, Sargsyan, Khachik, Çelebi, Mehmet, Taciroglu, Ertugrul
• Format: Journal Article
• Language: English
• Published: Pavia Wiley Subscription Services, Inc 01.11.2022; Wiley
• Subjects: Bayesian analysis; Bayesian updating; Calibration; Computer applications; Earthquake engineering; Earthquake prediction; Earthquakes; Finite element method; Mathematical models; modeling uncertainty; Modelling; OTHER INSTRUMENTATION; Predictions; regional seismic risk assessment; Risk assessment; Seismic activity; Seismic engineering; surrogate models; Uncertainty
• ISSN: 1545-2255; 1545-2263
• DOI: 10.1002/stc.3078

Summary 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. 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.