Refined physics-based models generally present a relevant number of parameters to calibrate against experimental data, which might be unavailable for the mixture or the service scenario of interest. This represents one of the most relevant issues in material modelling, especially when descriptive models are adapted to serve as predictive ones. Additionally, accurate small-scale models are particularly suitable for simulating laboratory tests. The up-scaling to structural members, or even entire structures, requires the identification of bridging parameters, responsible for bringing the small-scale models’ accuracy into the engineering models adopted for the long-term prediction at the structural level. This paper presents a methodological approach to deal with the uncertainty featuring the models calibration in case of limited experimental data. In addition, a strategy of up-scaling, relying on the fuzzy logical approach is presented. The activity performed is framed into the Horizon 2020 project ReSHEALience [1].