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The computational efficiency of a sampling based nonlinear Kalman filtering process is mainly conditional on the number of sigma/sample points required by the filter at each time step to effectively quantify statistical properties of related states and parameters. Efficaciously minimizing the required number of points would therefore have important implications, especially for large n-dimensional nonlinear systems. To this end, a Scaled Spherical Simplex Filter (S3F) with a decreased n+ 2 sigma points set size is presented in this paper, that can practically achieve the same accuracy level as the typical 2n+ 1 sigma points Unscented Kalman Filter (UKF), with almost 50% less computational requirements. The filtering framework is integrated with our recently developed fully parametrized damage-plasticity consistent hysteretic finite element modeling approach, enabling ideal compatibility in terms of computational implementation and performance for online state-parameter identification.