Automating the Seismic Design of Reinforced Concrete Rectangular Columns Employing Multi-Expression Programming

Abstract

This chapter explores an AI-powered approach for predicting the seismic capacity of RC rectangular columns using multi-expression programming (MEP). Leveraging a database of 250 RC column specimens tested under seismic loads from the Pacific Earthquake Engineering Research (PEER) Centre, two distinct MEP models were developed for flexural and shear capacity prediction. These models, trained with five key input variables using MEPX software, achieved high accuracy (R2 > 0.96) exceeding the performance of ACI 318-19 code provisions. Additionally, the models captured the underlying physical processes of seismic behavior in columns. These findings suggest the potential of MEP-based models for practical application in seismic design automation of RC structures.