This work presents a new staggered multilevel material identification procedure for phenomenological critical state plasticity models. The emphasis is placed on cases in which available experimental data and constraints are insufficient for calibration. The key idea is to create a secondary virtual experimental database from high-fidelity models, such as discrete element simulations, then merge both the actual experimental data and secondary database as an extended digital database to determine material parameters for the phenomenological macroscopic critical state plasticity model. The calibration procedure therefore consists of two steps. First, the material parameters of the DEM simulations are identified via the standard optimization procedure. Then, the calibrated DEM simulations are used to expand the experimental database with new simulated loading histories. This expansion of database provides additional constraints necessary for calibration of the phenomenological critical state plasticity models. The robustness of the proposed material identification framework is demonstrated in the context of the Dafalias-Manzari plasticity model.