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E. Sadeghipour Chahnasir,Y. Zandi,M. Shariati,E. Dehghani,A. Toghroli,E. Tonnizam Mohamad,A. Shariati,M. Safa,K. Wakil,M. Khorami 국제구조공학회 2018 Smart Structures and Systems, An International Jou Vol.22 No.4
The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.
E. Shafiei,K. Dehghani,F. Ostovan,Meysam Toozandehjani 대한금속·재료학회 2019 METALS AND MATERIALS International Vol.25 No.5
In this study, anisotropic elastic and plastic mechanical properties of a tailor rolled blank (TRB) with thickness ratio of0.52 (1 mm/1.9 mm), have been fully studied. To gain a deeper insight into anisotropic mechanical behavior of the studiedTRB, continuous changes in microstructure and crystallographic texture of a dual phase steel caused by variable gaugerolling (VGR) were investigated on several points (on RD–ND plane) along longitudinal direction with the aid of EBSDobservations. Analysis of grain boundary (GB) maps revealed that ferrite grain refinement is occurred during VGR so thatthe average ferrite grain size decreases from 4.1 μm for the thicker side to 2.2 μm for the thinner side. Furthermore, it wasfound out that substructure density is more intense within smaller ferrite grains. Evaluation of inverse pole figures as well asorientation distribution function maps showed that texture of thicker side comprises {001}⟨110⟩ and {112}⟨110⟩ componentsalong -fiber. In addition, it was revealed that orientations of {111}⟨110⟩ and {111}⟨112⟩ along -fiber strengthen alongVGR direction by further increase in thickness reduction near thinner side. Accordingly, unprecedented gradual changes inmechanical properties with respect to these changes in microstructure and texture, were obtained. Finally, the correlationbetween in-plane anisotropic mechanical properties [Young’s modulus (E), yield stress (YS), ultimate tensile stress (UTS)and total elongation] of the studied tailor rolled blank with microstructure and deformation texture was interpreted.
Chahnasir, E. Sadeghipour,Zandi, Y.,Shariati, M.,Dehghani, E.,Toghroli, A.,Mohamad, E. Tonnizam,Shariati, A.,Safa, M.,Wakil, K.,Khorami, M. 국제구조공학회 2018 Smart Structures and Systems, An International Jou Vol.22 No.4
The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.