Dynamic Constitutive Model of Steel Fiber Reinforced Concrete and Its Finite Element Method

Taking the split Hopkinson pressure bar(SHPB) test data as training samples, an implicit dynamic constitutive model of steel fiber reinforced concrete(SFRC) was proposed with the improved BP neural network program. On the basis of ABAQUS/Explicit, a user defined material subroutine VUMAT was compiled with FORTRAN language, embedding with the neural network constitutive model into finite element calculation. With SFRC structure＇s response under impact load as case study, it is shown that the constructed constitutive model and its finite element method are effective.     

Application of RBF Algorithm Based on Generalized Genetic Optimization in Prediction Model for Molten Iron Desulfuration

Desulfuration process is a very sophisticate reaction which is not only diverse but also non-line. A RBF algorithm based on generalized genetic optimization is proposed after studying the standard genetic and RBF algorithm. The authors also introduce its application in prediction Model for molten Iron Desulfuration. The algorithm perfectly resolve the problem of random selection of RBF cluster center number. Furthermore, it also reduces the time which GA uses. Comparison between the simulation results of RBF and RBF algorithm Based on GGA optimization further proves the efficiency and precision of its application in Prediction Model for Molten Iron Desulfuration. Finally the result of the test shows that after adopting the algorithm, the end-point hitting ratio can reach eighty-five percent. This indicate the algorithm has the engineering practicability.