Estimating the Strength of Bi-Axially Loaded Track and Channel Cold Formed Composite Column using Different AI-Based Symbolic Regression Techniques

Abstract

In this research work, the strength of bi-axially loaded track and channel cold formed composite column has been estimated by applying three AI-based symbolic regression techniques namely “Genetic Programming (GP)”, “Evolutionary Polynomial Regression (EPR)” and “Group Method of Data Handling Neural Network (GMDH-NN)”. The collected numerically generated data entries containing global slenderness ratio (Column height / minor radius of gyration) (λ), local slenderness ratio of channel (bolts spacing S2 / channel thickness) (λc), local slenderness ratio of track (bolts spacing S1 / track thickness) (λt), relative eccentricity in the major direction (ex/D) and the relative eccentricity in the minor direction (ey/B) as the independent parameters and the normalized average normal stress at failure (Ult. load /Area) / yield stress (F/Fy) as the dependent parameter. The results of the models were validated using the R², MAE and RMSE metrics. Both correlation and sensitivity analysis showed that the global slenderness ratio (λ) has the main influence on the strength, then the relative eccentricities (ex/D, ey/B) and finally the local slenderness ratios (λc, λt). Comparing predicted and calculated strengths showed that the three developed predictive models have the same level of accuracy (94%) with (R² > 0.965), (MAE < 0.03) and (RMSE < 0.03).