Axial compressive capacity prediction and optimal design of circular UHPC-filled steel tube based on Hybrid Symbolic Regression - Neural Network model

This study presents two methodologies for estimating the ultimate bearing capacity of ultra-high-performance concrete-filled circular steel tube (UHPCFST) columns and offers design optimization guidance: one employing a Symbolic Regression algorithm and the other a Hybrid Symbolic Regression - Neural Network (SR-NN) model. The models were trained, tested, and validated using experimental data from 498 samples sourced from existing literature. The optimal model, SR-NN_14_6, was identified from a pool of 480 models using a trial-and-error approach. The formula and model exhibited enhanced stability and precision, as indicated by their mean absolute error of 8.601 % and 8.051 %, respectively, marking an improvement exceeding 30 % relative to the best existing AIJ code. Using the validated model, a parametric analysis was conducted to assess the influence of the confinement effect coefficient () and steel ratio () on the strength index () of UHPCFST members, leading to the identification of two optimal parameter ranges. The optimal ranges of and were determined to be 0.84 to 1.29 % and 20 % to 30 %, respectively, offering valuable guidelines for the design of UHPCFST members.