Machine learning-based RC beam-column model parameter estimation and uncertainty quantification for seismic fragility assessment

Abstract

The simplicity and computational efficiency of lumped plasticity beam-column element models have been widely utilized for nonlinear response estimation of reinforced concrete (RC) frame elements under seismic shaking. At present, a prevalent approach for estimating modeling parameters includes linear regression-based semi-empirical equations developed after calibration of experimental column test results with varying design details. Since the choice of modeling parameters affects the prediction of complex seismic behavior, an underlying assumption of static linear relationships may not be valid. Furthermore, experimental column test data is limited and typically sourced from multiple independent studies, reflecting significant heterogeneity prevailing in column properties. Consequently, the homoskedastic assumption of the prediction uncertainty in linear regression is questionable. This study addresses the above drawbacks through a Gaussian process regression (GPR) approach that is capable of analyzing nonlinear patterns in data sets, despite small sample sizes. The kernel-based framework of GPR also efficiently estimates the pointwise prediction uncertainty as opposed to the homoskedastic assumption of linear regression. Using a case study example of an archetypical reinforced concrete moment resisting frame building, the prediction uncertainty is propagated in the collapse fragility framework. Results reveal that overall fragility uncertainty can substantially change through the consideration of pointwise prediction uncertainty and better fitting models.