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The paper presents the results of investigating the strength characteristics of eleven compositions of protective and decorative epoxy polymer coatings with mineral fillers and an unfilled polymer under natural climatic aging in a temperate climate (Saransk). The work formalises the methodological approach to the formation of a training dataset for predicting changes in the strength characteristics of polymer composites with mineral fillers using machine learning methods. It is shown that with the introduction of a mineral filler, the failure mode of the epoxy matrix under bending changes from viscous-fluid to brittle, which allows the bending strength to be unambiguously determined. Under uniaxial compression, the classical strength value is reached in the region of relative strains of 50 % and higher, which corresponds to the densification phase of the already failed material and does not physically reflect the actual strength response. As a methodologically correct alternative, the yield strength is proposed, identified as the first local maximum on the smoothed σ–ε curve and unambiguously determined for all studied compositions at all natural climatic aging points. The relative difference Δσ between the strength value (in bending) or the yield strength (in compression) of the filled composition and the corresponding stress value on the deformation curve of the unfilled polymer, taken at the same point of relative strains for each time point of climatic aging, is proposed as the target feature of the prediction model. This approach automatically eliminates from the target variable the components associated with the post-curing effects and the degradation of the polymer matrix itself, leaving the model with the task of predicting the contribution of the filler and the state of the polymer–filler interface. Based on the analysis of the reinforcing effect of the eleven filled compositions in the reference state without post-curing, three groups of fillers are identified according to the level of the relative strength gain under bending («low» 3÷10 %, «moderate» 24÷53 %, «high» 70÷120 %), which are consistent with the physical picture of the filler–polymer matrix interaction. Recommendations are formulated on the composition of the model's input feature space, including the type and shape of the filler particles, the mass fraction, the characteristic particle size, the specific surface area, and the characteristic relative strain of the composition in the reference state. It is noted that the bending tests possess substantially greater informativeness in the assessment of the climatic resistance of the investigated class of materials in comparison with uniaxial compression.
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2. Nizin D.R. Klimaticheskaya stoykost’ zashchitno-dekorativnykh pokrytii na osnove modifitsirovannykh epoksidnykh svyazuyushchikh [Climatic resistance of protective and decorative coatings based on modified epoxy binders]. Cand. Sci. (Eng.) Dissertation. Saransk, 2017. 216 p.
3. Xanthos M. (Ed.). Funktsional’nye napolniteli dlya plastmass [Functional Fillers for Plastics]. Translated from English under the editorship of V.N. Kuleznev. Saint Petersburg: Nauchnye osnovy i tekhnologii, 2010. 576 p.
4. Kablov E.N., Startsev V.O., Laptev A.B. Starenie polimernykh kompozitsionnykh materialov [Aging of polymer composite materials]. Moscow: NITS «Kurchatovskiy institut» – VIAM, 2023. 536 p.
5. Pickett J.E. Weathering of plastics // Handbook of Environmental Degradation of Materials. 3rd ed. Amsterdam: Elsevier, 2018. P. 163 – 184. DOI: 10.1016/B978-0-323-52472-8.00008-3
6. Collings T.A. Moisture Management and Artificial Ageing of Fibre Reinforced Epoxy Resins // Composite Structures 5. Springer, Dordrecht, 1989. P. 213 – 239. DOI: 10.1007/978-94-009-1125-3_9
7. Startsev O.V., Erofeev V.T., Selyaev V.P. (Eds.). Klimaticheskie ispytaniya stroitel’nykh materialov [Climatic testing of construction materials]. Moscow: Izdatel’stvo ASV, 2017. 558 p.
8. He W., Jiang X., He R. et al. A review on the aging behavior of BADGE-based epoxy resin. Buildings. 2025. 15 (14). Article 2450. DOI: 10.3390/buildings15142450
9. Delor-Jestin F., Drouin D., Cheval P.-Y., Lacoste J. Thermal and photochemical ageing of epoxy resin – Influence of curing agents. Polymer Degradation and Stability. 2006. 91 (6). P. 1247 – 1255. DOI: 10.1016/j.polymdegradstab.2005.09.009
10. Schmidt J., Marques M.R.G., Botti S., Marques M.A.L. Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials. 2019. 5. Article 83. DOI: 10.1038/s41524-019-0221-0
11. Sharma A., Mukhopadhyay T., Rangappa S.M., Siengchin S., Kushvaha V. Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design. Archives of Computational Methods in Engineering. 2022. 29 (5). P. 3341 – 3385. DOI: 10.1007/s11831-021-09700-9
12. Wang J., Karimi S., Zeinalzad P., Zhang J., Gong Z. Using machine learning and experimental study to correlate and predict accelerated aging with natural aging of GFRP composites in hygrothermal conditions. Construction and Building Materials. 2024. 438. Article 137264. DOI: 10.1016/j.conbuildmat.2024.137264
13. Xu P., Ji X., Li M., Lu W. Small data machine learning in materials science. npj Computational Materials. 2023. 9. Article 42. DOI: 10.1038/s41524-023-01000-z
Nizin D.R., Nizina T.A., Spirin I.P., Pivkin N.A. Methodological aspects of training dataset construction for predicting the mechanical strength of filled polymer coatings during natural climatic aging. Construction Materials and Products. 2026. 9 (4). 3. https://doi.org/10.58224/2618-7183-2026-9-4-3