AUTHOR=Khodadadi Koodiani Hamid , Majlesi Arsalan , Shahriar Adnan , Matamoros Adolfo 

TITLE=Non-linear modeling parameters for new construction RC columns

JOURNAL=Frontiers in Built Environment

VOLUME=Volume 9 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2023.1108319

DOI=10.3389/fbuil.2023.1108319

ISSN=2297-3362

ABSTRACT=This paper introduces equations to calculate reinforced concrete column nonlinear modeling parameters for design verification of new buildings using response history analysis. The proposed equations are intended for use with provisions in Appendix A of ACI 318-19 and Chapter 16 of ASCE/SEI 7. The equations developed in the study are for inelastic deformation capacity parameters (MPs) anl and bnl that define load-deformation envelop relationships of column elements in nonlinear models. The proposed equations were calibrated using the ACI Committee 369 column database, which includes column configuration parameters, material properties, and deformation capacity MPs inferred from the measured response of columns under load reversals. Dimension reduction techniques were applied to visualize different clusters of data in 2D space using the negative log-likelihood score. This technique allowed decreasing the nonlinearity of the problem by identifying a subset of columns with load-deformation behavior representative of new construction conforming to current codes requirements. A Neural Network model (NN) was calibrated and used to perform parametric variations to identify the most relevant input parameters and characterize their effect on MPs. Linear regression models including the most relevant features from the parametric study were calibrated for rectangular and circular columns. The proposed linear regression equations were found to provide better estimates of new construction column MPs than the Tables in ACI 374.3R and ASCE 41-13, and the equations ASCE 41-17.