Physical Parameter Identification of Structural Systems with Hysteretic Pinching

This research investigates the physical parameter identification of a nonlinear hysteretic structure with pinching behavior for real-time or rapid structural health monitoring (SHM) after a major seismic event. The identification procedure is based on the overall least squares linear regression and hypothesis testing. It is applied to a general, nonlinear slip-lock (SL) pinching model. In particular, the hysteresis loop is reconstructed using data available from current sensor technologies. The path dependent hysteresis response is first divided into different loading and unloading subhalf cycles with a single valued function. These subhalf cycles are then assumed to be piecewise linear, and the number of segments for each subhalf cycle is identified using the sup F type test. The overall least squares linear regression is finally applied to the identified subhalf cycles to compute the regression coefficients and breakpoints that yield the elastic stiffness, plastic stiffness, and cumulative plastic deformation. The performance and robustness of the proposed method is illustrated using a single degree of freedom shear-type reinforced concrete structure with 10% added root mean square noise and variable pinching behavior. The proposed method is shown to be computationally efficient and accurate in identifying the damage parameters within 10% of true values. These results indicate that the system is able to capture nonlinear behavior and structural parameters, such as preyielding stiffness, postyielding stiffness and cumulative plastic deformation, directly relevant to damage and performance using a computationally efficient and simple method. Finally, the method requires no user input and could thus be automated and performed in real time for each half cycle, with results available effectively immediately after an event, as well as during an event, if required.