Nonlinear Analysis Of Squar Tubular Truss Structures With Semi-Regid Connection

As to steel joints, deforming performances deserve special attention, because they closely correlate to the simplified analysis,stress and deformation of the structure. Accordingly, this paper analyzes local deformation and local stiffness of square tube joint as the following:1. The effect of joint deformation to truss displacement: 4 kinds of steel tube truss compute models are established making use of shell elements, truss elements, beam elements and combination of beam elements and truss elements. Trough calculation, it is obviously reasonable to ignore the effect of joint bending stiffness and consider it as a hinge joint in calculation. But it is inaccurate to ignore the effect of joint's local translational stiffness and consider it as an ideal hinge joint. Model of series spring truss elements is put forward according to square tubular joint's local stiffness in the paper, which meets the designing requirements and is testified by numerical examples.2. Analysis of translational displacement and translational stiffness of steel tube joints: the stiffness distribution, material property and transfer path of load determines the joint's bearing capacity and failure modes, and also determines its deforming performance and the effect of its local stiffness to the whole structure. Finite element method and nonlinear regression method is used to discuss the relationship of Y shape steel tube joint's local translational displacement and local translational stiffness to the ratio between the width and the thickness of main tube, ratio of width and thickness between branch tube and main tube and included angle between branch tube and main tube's the axial directions. And it is verified by test of significance of the regression equation and regression coefficient.3. Analysis of K shape joint's local translational displacement and BP neural network simulation: this paper takes orthogonal test method to analyze the significance of the influence of the parameters to local translational displacement. BP neural network is set to simulate the parameters'variation, and would have good performance if trained repeatedly, and then its favorable accuracy is proved by a new numerical example.4. Analysis of deformation and plastic coefficient of square tube joints: (1) As to Y-shape joint under design load, while the main tube is in tension, the joint deformation is comparatively great and the joint bearing capacity increases as it escalates, and the joint has good ductility; while the main tube is in compression, the joint deformation is comparatively small and the joint does not have good ductility. Plastic coefficientχdose not change much with each parameter but when the stress state of the main tube differs. (2) As to K-shape joint under design load, while the main tube is in tension, the joint bearing capacity increases as the joint deformation escalates, and the joint has good ductility; when the main tube is in compression, despite taking adverse impact into account, the joint bearing capacity increases as the joint deformation escalates, and the joint has good ductility. Plastic coefficientχwould change much only when the stress state of the main tube differs.