Non-linear modeling parameters for new construction RC columns

Modeling parameters (MP) of reinforced concrete columns are a critical component performance-based seismic assessment methodologies because in these approaches damage is estimated based on element deformations calculated using non-linear models. To ensure model fidelity and consistency results, methods ASCE 41, ACI 369.1, 374.3R prescribe modeling calibrated experimental data. This paper introduces new set equations to calculate column optimized for design verification buildings response history analysis. Unlike provided 369.1 intended older non-ductile buildings, the nl b presented this study were simulate load-deformation envelope that meet detailing requirements modern codes. Specifically, proposed use with provisions 374.3R, Chapter 18 Appendix A 318-19 16 ASCE/SEI 7-16. The Committee 369 database, which includes configuration parameters, material properties, deformation capacity inferred from measured under load reversals. Dimension reduction techniques applied visualize different clusters data 2D space negative log-likelihood score. technique allowed decreasing non-linearity problem by identifying subset behavior representative construction conforming current codes requirements. Neural Network (NN) was used perform parametric variations identify most relevant input characterize their effect stablish degree between each variable output. Developing applicable wide range challenging, so research considered subsets database calibrate simple practical equations. Linear regression models including features rectangular circular columns. linear found provide better estimates than available tables 41-13, 41-17.