A 3D finite element model updating of spinal lumber segment applying experimental modal data and particle swarm optimization algorithm

Abstract

The dynamic loadings can be more harmful than static ones for the human lumbar spine health. Consequently, the prediction of the spine behavior under dynamic and vibration loads is vital and can be achieved by creating a precise finite element model in which the dynamic mechanical properties of the components need to be estimated. For this purpose, noninvasive experimental modal analysis can be applied to evaluate the dynamic mechanical properties of the spine in the numerical simulation by supplying the structural dynamic characteristics (natural frequencies, mode shapes, and damping ratio) of the system. Since the most adequate model for the human lumbar segment is a sheep model, in this paper, a 3D finite element model of a fresh spinal lumber segment of a sheep is generated based on the poroelasticity theory via Abaqus and is updated utilizing particle swarm optimization (PSO) algorithm and experimental modal analysis. In this regard, the frequency response function (FRF) of the specimen is obtained by performing the experimental modal test and the modal parameters are derived by using the rational fraction polynomial method. Afterward, the sensitivity analysis is carried out to determine the appropriate design variables. Finally, the PSO algorithm using experimental data is employed to update the design variables, including the elastic material properties and the structural damping factors of the specimen components, and the stiffness and damping coefficient of the suspension system. According to the results, the error percentage between the numerical FRF and the experimental one decreases remarkably after model updating indicating the high efficiency of the methods used.