Русский
English
Based on the experimental data of concrete nonlinear creep under high stress levels (40-80% of prismatic strength), this study explores the application of machine learning methods for predicting creep deformation. A recurrent artificial neural network (ANN) and the CatBoost algorithm were employed to model the time-dependent creep strain, using stress and time as input parameters. The ANN demonstrated high predictive accuracy, with training achieving a mean square error of 0.000154, and its generated creep curves showed an excellent fit with the experimental data. In contrast, the CatBoost algorithm, while effectively capturing the physical trend that creep strain increases nonlinearly with stress and decelerates over time, exhibited lower prediction accuracy than the ANN. Feature importance analysis within the CatBoost model highlighted the significant influence of lagged stress parameters and time-squared terms, aligning with the nonlinear physical nature of concrete creep. The results confirm the strong potential of machine learning, particularly recurrent neural networks, for modeling complex nonlinear creep in concrete, even with limited datasets. Future work is suggested to incorporate concrete strength class and loading age as additional parameters to enhance model generalizability.
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24. Degefa A.B., Girum S., Lotherbach B., Amorim Júnior N.S., Haha M.B. Data-Driven Insights into Controlling the Reactivity of Supplementary Cementitious Materials in Hydrated Cement. International Journal of Concrete Structures and Materials. 2024. 18. P. 39. DOI: 10.1186/s40069-024-00677-w
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26. Lee N.K., Choi Y., Lee H.K. Modeling of Temperature Evolution and Thermal Stress in Mass Concrete with Ground Granulated Blast-Furnace Slag. Construction and Building Materials. 2021. 266. P. 120974. DOI: 10.1016/j.conbuildmat.2020.120974
27. Chepurnenko A.S., Turina V.S., Akopyan V.F. Processing of nonlinear concrete creep curves using nonlinear optimization methods. Construction Materials and Products. 2024. 7 (1). 2. DOI: 10.58224/2618-7183-2024-7-1-2
28. Кlyuev S.V., Klyuev A.V., Аyubov N.А., Fediuk R.S., Levkina Е.V. Finite Element Design and Analysis of Sustainable Mono-Reinforced and Hybrid-Reinforced Fibergeopolymers. Advanced Engineering Research (Rostov-on-Don). 2025. 25 (3). P. 171 – 185. DOI: 10.23947/2687-1653-2025-25-3-171-185
29. Litvinov S.V., Yazyev B.M., Kuznetsov V.V., Belyugin N.V., Avakov A.A. Study of the concordance between various concrete deformation models and experimental data for uniaxial compression cases. Construction Materials and Products. 2024. 7 (5). 6. DOI: 10.58224/2618-7183-2024-7-5-6
2. Wei Y., Huang J., Liang S. Measurement and modeling concrete creep considering relative humidity effect. Mechanics of Time-Dependent Materials. 2020. 24. P. 161 – 177. DOI: 10.1007/s11043-019-09415-2
3. Zhang C., Wang P., Gao M., Wang Z. Nonlinear creep damage constitutive model of concrete based on fractional calculus theory. Materials. 2019. 12 (9). P. 1505. DOI: 10.3390/ma12091505
4. Mussabayev T.T., Kussaiyn-Murat A., Ospanova S., Karymsakova D., Tleukenova U. Creep of Concrete in Shell Structures: Nonlinear Theory. Materials. 2023. 16 (16). P. 5587. DOI: 10.3390/ma16165587
5. Nastic M., Tazarv M., Moustafa M.A. Shrinkage and creep strains of concrete exposed to low relative humidity and high temperature environments. Nuclear Engineering and Design. 2019. 352. P. 110154. DOI: 10.1016/j.nucengdes.2019.110154
6. Dummer A., Neuner M., Hofstetter G. An extended gradient-enhanced damage-plasticity model for concrete considering nonlinear creep and failure due to creep. International Journal of Solids and Structures. 2022. 243. P. 111541. DOI: 10.1016/j.ijsolstr.2022.111541
7. Yu P., Zhang J., Li L., Zhou C. A coupled creep and damage model of concrete considering rate effect. Journal of Building Engineering. 2022. 45. P. 103621. DOI: 10.1016/j.jobe.2021.103621
8. Liu W., He S., He Z., Liu Z. Constitutive model of concrete creep damage considering the deterioration of creep parameters. Construction and Building Materials. 2021. 308. P. 125047. DOI: 10.1016/j.conbuildmat.2021.125047
9. Pan Z., Feng D., Wu T. Nonlinear Creep Amplification Factor Considering Damage Evolution of Concrete under Compression. Materials. 2022. 15 (19). P. 6742. DOI: 10.3390/ma15196742
10. Ren X., Li J., Li K., Fan H. Coupled creep-damage-plasticity model for concrete under long-term loading. Journal of Engineering Mechanics. 2020. 146 (5). P. 04020027. DOI: 10.1061/(ASCE)EM.1943-7889.0001757
11. Zhou X., Pan X., Berto F. A state-of-the-art review on creep damage mechanics of rocks. Fatigue & Fracture of Engineering Materials & Structures. 2022. 45 (3). P. 627 – 652. DOI: 10.1111/ffe.13630
12. Liu Y., He X., Liu C., Liu Y. Nonlinear creep behavior and viscoelastic-plastic constitutive model of rock-concrete composite mass. Advances in Civil Engineering. 2020. 2020. P. 1-14. DOI: 10.1155/2020/8875760
13. Li Y., Zhang Y., Liu H. Verification of concrete nonlinear creep mechanism based on meso-damage mechanics. Construction and Building Materials. 2021. 276. P. 122205. DOI: 10.1016/j.conbuildmat.2020.122205
14. Tsitova A., Wadsö L., Šavija B. Experimental and numerical analyzes of the interaction of creep with mesoscale damage in cementitious materials. Mechanics of Materials. 2023. 184. P. 104715. DOI: 10.1016/j.mechmat.2023.104715
15. Ma G., Li J., Chen R., He P. Mesoscale investigation on concrete creep behaviors based on discrete element method. Construction and Building Materials. 2022. 342. P. 127957. DOI: 10.1016/j.conbuildmat.2022.127957
16. Zhu J., Wang Y. Convolutional neural networks for predicting creep and shrinkage of concrete. Construction and Building Materials. 2021. 306. P. 124868. DOI: 10.1016/j.conbuildmat.2021.124868
17. Li K., Xu L., Wang S., Zhao L., Fan J. Modeling and sensitivity analysis of concrete creep with machine learning methods. Journal of Materials in Civil Engineering. 2021. 33 (8). P. 04021206. DOI: 10.1061/(ASCE)MT.1943-5533.0003815
18. Faridmehr I., Bedon C., Nikoo M., Husek M., Nejad F.M., Trung N.-T. Novel hybrid informational model for predicting the creep and shrinkage deflection of reinforced concrete beams containing GGBFS. Neural Computing and Applications. 2022. 34 (15). P. 13107 – 13123. DOI: 10.1007/s00521-022-07148-x
19. Liang M., Chang Z., Wan Z., Gan Y., Schlangen E., Šavija B. Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete. Cement and Concrete Composites. 2022. 125. P. 104295. DOI: 10.1016/j.cemconcomp.2021.104295
20. Feng J., Ren Q., Liang M., Li W., Gan Y., Schlangen E., Šavija B. A machine learning and game theory-based approach for predicting creep behavior of recycled aggregate concrete. Case Studies in Construction Materials. 2022. 17. P. e01653. DOI: 10.1016/j.cscm.2022.e01653
21. Li W., Feng J., Ren Q., Liang M., Gan Y., Šavija B. Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods. Buildings. 2024. 14 (11). P. 3627. DOI: 10.3390/buildings14113627
22. Li C., Liang M., Wang Z., Šavija B. A parameterless approach for long-term creep prediction in concrete using hybrid CNN-transformer model. Structural Health Monitoring. 2025. DOI: 10.1177/14759217251357971
23. Zhu P., Feng J., Li W., Liang M., Šavija B. Interpretable Machine Learning Models for Prediction of UHPC Creep Behavior. Buildings. 2024. 14 (7). P. 2080. DOI: 10.3390/buildings14072080
24. Degefa A.B., Girum S., Lotherbach B., Amorim Júnior N.S., Haha M.B. Data-Driven Insights into Controlling the Reactivity of Supplementary Cementitious Materials in Hydrated Cement. International Journal of Concrete Structures and Materials. 2024. 18. P. 39. DOI: 10.1186/s40069-024-00677-w
25. Adsul N., Choi Y., Kang S.T. A Comprehensive Review of Numerical and Machine Learning Approaches for Predicting Concrete Properties: From Fresh to Long-Term. Materials. 2025. 18 (15). P. 3718. DOI: 10.3390/MA18153718
26. Lee N.K., Choi Y., Lee H.K. Modeling of Temperature Evolution and Thermal Stress in Mass Concrete with Ground Granulated Blast-Furnace Slag. Construction and Building Materials. 2021. 266. P. 120974. DOI: 10.1016/j.conbuildmat.2020.120974
27. Chepurnenko A.S., Turina V.S., Akopyan V.F. Processing of nonlinear concrete creep curves using nonlinear optimization methods. Construction Materials and Products. 2024. 7 (1). 2. DOI: 10.58224/2618-7183-2024-7-1-2
28. Кlyuev S.V., Klyuev A.V., Аyubov N.А., Fediuk R.S., Levkina Е.V. Finite Element Design and Analysis of Sustainable Mono-Reinforced and Hybrid-Reinforced Fibergeopolymers. Advanced Engineering Research (Rostov-on-Don). 2025. 25 (3). P. 171 – 185. DOI: 10.23947/2687-1653-2025-25-3-171-185
29. Litvinov S.V., Yazyev B.M., Kuznetsov V.V., Belyugin N.V., Avakov A.A. Study of the concordance between various concrete deformation models and experimental data for uniaxial compression cases. Construction Materials and Products. 2024. 7 (5). 6. DOI: 10.58224/2618-7183-2024-7-5-6
Kondratieva T.N., Chepurnenko A.S. Prediction of concrete nonlinear creep using machine learning methods. Construction Materials and Products. 2026. 9 (1). 2. https://doi.org/10.58224/2618-7183-2026-9-1-2