This paper presents the application of finite state machine (FSM) theory to the programming of nonlinear hysteretic model simulation for both known and newly created rules. The complicated reversed internal paths invo...This paper presents the application of finite state machine (FSM) theory to the programming of nonlinear hysteretic model simulation for both known and newly created rules. The complicated reversed internal paths involved in the nonlinear relationship which not only depend on material properties, but also on load history, often confuse rule creators and scholars. In this paper, we first describe the development of past hysteretic models. Then we introduce the FSM theory conceptually, and explain how it is applied to reversed and diverse routes. Next, state definitions and procedures are explained with a specific data example using the bilinear model. Finally, the successful application to UC-win/FRAME (3D) is described and several characteristics are summarized. By using FSM’s states and the linkages to represent a hysteresis model, we can quickly realize the programming of the defined complex model rules, and the nonlinear modeling becomes more efficient and feasible.展开更多
文摘This paper presents the application of finite state machine (FSM) theory to the programming of nonlinear hysteretic model simulation for both known and newly created rules. The complicated reversed internal paths involved in the nonlinear relationship which not only depend on material properties, but also on load history, often confuse rule creators and scholars. In this paper, we first describe the development of past hysteretic models. Then we introduce the FSM theory conceptually, and explain how it is applied to reversed and diverse routes. Next, state definitions and procedures are explained with a specific data example using the bilinear model. Finally, the successful application to UC-win/FRAME (3D) is described and several characteristics are summarized. By using FSM’s states and the linkages to represent a hysteresis model, we can quickly realize the programming of the defined complex model rules, and the nonlinear modeling becomes more efficient and feasible.
