According to 2013/59/Euratom Directive, the activity concentration index (ACI) is required to be estimated for each building material that is of concern from a radiation protection point of view. This index applies to building materials and not to constituents that cannot be used as building materials themselves. Fly ash is a byproduct of coal-fired power plants and is one of the main constituents of cement. The radioactivity in fly ash that is produced by Greek lignite power plants cannot be considered insignificant. For example, in the case of the Megalopolis power plant, the concentration for radioisotopes of the ²²⁶Ra chain is found to be about 1 kBq/kg. Since natural radionuclide concentrations, which are harmful to human health in terms of radiation exposure, exist in fly ash, ACI should be assessed for building materials containing fly ash. The present study evaluates the ACI of concrete containing fly ash cement when used in multistory residential buildings. Results showed that cement produced in Greece by the three main Greek cement production plants, containing lignite fly ash, and used as a material for concrete multistory constructions, should not be considered as “of concern from a radiation protection point of view”. Each country that wishes to evaluate the use of fly ash into constructions should repeat the method for the ACI uncertainty budget proposed in this study, to assess whether it significantly exceeds the reference value (whether it is of concern from a radiation protection point of view).

The Activity Concentration Index (ACI), defined in Directive 2013/59/Euratom, serves as a criterion for the radiological significance of Naturally Occurring Radioactive Materials (NORMs) concentrated in building materials, considering related exposures due to the external gamma radiation field but not due to radon concentration levels. This study proposes a simple way of applying the ACI to interior spaces when concrete is the dominant construction material. Three calculation methods were examined, using four spaces within existing buildings, namely Method A, using the building elements’ mass proportions as a weighting factor; Method B, using only the geometrical characteristics of the internal space; and Method C, combining the mass proportions and inverse square distances. This methodology proposes a way of calculating the ACI based on data provided by existing studies about NORM concentrations in building materials and, thus, no sampling and subsequent NORM concentration measurements were required. The spatial data could be easily determined using either building plans or in situ measurements, using a handheld laser distance meter. The advantages and disadvantages of all three methods were analyzed, along with a comparison to in situ gamma radiation field measurements, performed with a portable Geiger–Müller detector. All the methods showed proportionality to the measured values. Method C was found to be the most suitable, especially for existing buildings, and Method A is recommended for early-stage design assessments.

Destructive testing evaluates material strength but prohibits repeated measurements on the same specimen. Finite element simulations, such as those using ANSYS Mechanical, provide a cost-effective alternative by delivering deterministic solutions under defined conditions. To incorporate input variability, the Monte Carlo method applies assigned probability distributions to parameters like magnitude and angle of the applied force and geometric tolerances of the specimen. Thus, input variability yields distributions for output such as stress and deformation, enabling uncertainty quantification. In this study, an example of static force on a concrete cube was modeled in ANSYS, and uncertainty was propagated using the Monte Carlo method, as described in JCGM 101:2008. This approach enables the identification of critical factors affecting the outcome and provides confidence intervals that might be used as decision rules to support comparisons of numerical simulations with experimental data and of results of different models and to calculate the limits of quality control charts of a testing laboratory.