Parametric Identification for a Truss Structure Using Axial Strain

The increasing use of advanced sensing technologies such as optic fiber Bragg grating and embedded piezoelectric sensors necessitates the development of strain-based identification methodologies. In this study, a three-step neural networks based strategy, called direct soft parametric identification (DSPI), is presented to identify structural member stiffness and damping parameters directly from free vibration-induced strain measurements. The rationality of the strain based DSPI methodology is explained and the theoretical basis for the construction of a strain-based emulator neural network (SENN) and a parametric evaluation neural network (PENN) are described according to the discrete time solution of the state space equation of structural free vibration. The accuracy, robustness, and efficacy of the proposed strategy are examined using a truss structure with a known mass distribution. Numerical simulations indicate that the average relative errors of identified structural properties were less than 5% and relatively insensitive to measurement noises.