Optimizing the Nonlinear Response of Elliptical Tubular Composite Beams Using SVM

Nonlinear behavior of elliptical concrete filled steel tubular (ECFST) beams was investigated in this work to optimize and model the effect of different parameters.ANSYS computer program has been used to analyze the three-dimensional models.The nonlinear material and geometrical analysis based on incremental iterative load method, is adopted.Parametric studies were carried out to identify the influence of key parameters on the behavior of moment (M) versus lateral displacement (Δ) relationship and effect of these parameters on strength index.These parameters include effect of thickness of steel tube, compressive strength of concrete, yielding strength of steel tube, shear span to depth ratio, and aspect ratio.It is concluded that the moment carrying capacity and strength index are increased as thickness, ′ , , and aspect ratio are increased.Meanwhile increasing shear span to depth ratio bring down the moment carrying capacity and strength index.The numerical analysis was integrated with artificial intelligence techniques Neural Network (ANN) and support vector machine (SVM) through the representation of the required number of models accomplished by adopting the ANSYS program, then using the generated data as inputs for ANN and SVM to developed the prediction model, these steps was verified to ensure the precise results.Finally, the results obtained through the parametric study was introduced to (ANN) model reflect high correlation (98.5%) with low root mean square error (0.043-0.185), and for (SVM) model reviled high correlation (98.3%) with minimal root mean square error (0.049-0.088).Both techniques were accurate but SVM was more reliable in term of minimal errors.The results of this innovative integrated approach proved the ability of achieving responses prediction for different sampling with low cost and sufficient accuracy for the design and analysis of ECFST beams.