TITLE:
Evaluation of Stiffened End-Plate Moment Connection through Optimized Artificial Neural Network
AUTHORS:
Mehdi Ghassemieh, Mohsen Nasseri
KEYWORDS:
End-Plate Moment Connection; Finite Element Method; Artificial Neural Network; Sensitivities Analysis; Genetic Algorithm
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.5 No.3,
March
29,
2012
ABSTRACT: This study involves the development of an analytical model for understanding the behavior of the extended, stiffened end-plate moment connections with eight high strength bolts. Modeling of the connection as an assemblage of finite elements (FE) used for load deformation analysis, with material, and contact nonlinearities are developed. Results from the FE mathematical model are verified with results from the ANSYS computer program as well as with the test results. Sensitivity and feasibility studies are carried out. Significant geometry and force related variables are introduced; and by varying the geometric variables of the connections within a practical range, a matrix of test cases is obtained. Maximum end-plate separation, maximum bending stresses in the end-plate, and the forces from the connection bolts for these test cases are obtained. From the FE analysis, a database is produced to collect results for the artificial neural network analysis. Finally, salient features of the optimized Artificial Neural Network (ANN) via Genetic Algorithm (GA) analysis are introduced and implemented with the aim of predicting the overall behavior of the connection.