Neural Parameter Calibration for Identification of Nonlinear Hysteretic Behavior in Bolted Joints

Abstract

A neural calibration framework is presented for estimating the nonlinear hysteretic behavior of jointed structures using the parameters of the Bouc-Wen model, which serves as a reduced-order representation of bolted joints under dynamic excitation. This methodology integrates data-driven modeling, physics-based representation, and ensemble-based variability analysis to achieve robust parameter estimation and to propagate parameter dispersion into response-level uncertainty envelopes. A feed-forward neural network is employed to minimize the discrepancy between simulated and measured responses, with multiple random initializations producing an ensemble of calibrations. This ensemble quantifies variability resulting from both neural initialization and experimental repetitions under identical boundary conditions. Experimental validation was performed on a beam testbed subjected to different vibration regimes and various torque-tightening levels. The results demonstrate that the calibrated models capture key hysteretic features, such as stiffness degradation and energy dissipation, while yielding physically admissible parameter sets and consistent uncertainty envelopes across repeated measurements. The evolution of these parameters may serve as health indicators for structural health monitoring (SHM), facilitating the development of uncertainty-aware digital shadows for nonlinear structural systems.