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The introduction of intelligent models, in particular using machine learning methods, opens up prospects for the development of the construction industry. The construction of regression models for predicting the physical and mechanical properties of various types of building materials is a promising and relevant area. The use of such models makes it possible to take into account complex and multifactorial dependencies, while minimizing the influence of the human factor. In the present study, variatropic concrete B30, obtained by centrifugation, acts as the test material. The dataset (351 objects) was assembled during laboratory studies to study the effect of freeze-thaw cycles on the strength characteristics of the material. Using the computer vision method based on the convolutional neural network U-Net, the damage on each of the concrete layers was assessed on different cycles. 4 machine learning models for predicting compressive strength were trained and tested on the collected dataset: Ridge Regression (RR), Random Forest (RF), CatBoost (CB) and Multi-layer Perceptron (MLP). The hyperparameters of the models were optimized using Grid Search + 3-fold cross-validation. As a result of testing the algorithms on a test sample, the best quality metrics were demonstrated by tree architectures: MAE for RF and CB 0.09 and 0.17 MPa, respectively, R2 = 0.99. The results are supplemented by SHAP analysis. The results obtained are a useful tool for optimizing the composition of variatropic concretes used under aggressive conditions.
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26. Li W., Wang H., Liu Z., Li N., Zhao S., Hu S, Steel Slag Accelerated Carbonation Curing for High-Carbonation Precast Concrete Development. Materials. 2024. 17 (12). P. 2968. DOI: 10.3390/ma17122968
27. Gołaszewski J., Gołaszewska M., Cygan G. Performance of Ordinary and Self-Compacting Concrete with Limestone after Freeze–Thaw Cycles. Buildings. 2022. 12 (11). P. 2003. DOI: 10.3390/buildings12112003
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29. Mailyan L.R., Stel'makh S.A., Shcherban E.M., Zherebtsov Yu.V., Al-Tulaikhi M.M. Research of physicomechanical and design characteristics of vibrated, centrifuged and vibrocentrifuged concretes. Advanced Engineering Research. 2021. 21 (1). P. 5 – 13. DOI: 10.23947/2687-1653-2021-21-1-5-13
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31. Kliukas R., Lukoševičienė O., Jaras A., Jonaitis B. The Mechanical Properties of Centrifuged Concrete in Reinforced Concrete Structures. Applied Sciences. 2020. 10 (10). P. 3570. DOI: 10.3390/app10103570
32. Feng B., Zhu Y.-H., Xie F., Chen J., Liu C.-B. Experimental Investigation and Design of Hollow Section, Centrifugal Concrete-Filled GFRP Tube Columns. Buildings. 2021. 11 (12). P. 598. DOI: 10.3390/buildings11120598
33. Stel’makh S.A., Shcherban’ E.M., Kholodnyak M.G., Nasevich A.S., Chernil’nik A.A. A device for manufacturing products from centrifuged concrete. Federation Patent 192492. 2019. https://patentimages.storage.googleapis.com/08/4b/f1/4688b6218e3156/RU192492U1.pdf.
34. Mailyan L.R., Stel’mak S.A., Shcherban’ E.M., Khalyushev A.K., Smolyanichenko A.S., Sysoev A.K., Parinov I.A., Cherpakov A.V. Investigation of Integral and Differential Characteristics of Variatropic Structure Heavy Concretes by Ultrasonic Methods. Applied Sciences. 2021. 11 (8). P. 3591. DOI: 10.3390/app11083591
35. Zubarev K.P., Razveeva I., Beskopylny A.N., Stel’makh S.A., Shcherban’ E.M., Mailyan L.R., Shakhalieva D.M., Chernil’nik A., Nikora N.I. Predicting the Strength of Heavy Concrete Exposed to Aggressive Environmental Influences by Machine Learning Methods. Buildings. 2025. 15 (21). P. 3998. DOI: 10.3390/buildings15213998
36. Beskopylny A.N., Stel’makh S.A., Shcherban’ E.M., Razveeva I., Kozhakin A., Meskhi B., Chernil’nik A., Elshaeva D., Ananova O., Girya M., Nurkhabinov T., Beskopylny N. Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete. Sensors. 2024. 24 (13). P. 4373. DOI: 10.3390/s24134373
37. Breiman L. Random Forests. Machine Learning. 2001. 45. P. 5 – 32. DOI: 10.1023/A:1010933404324
38. Liashchynskyi P, Pavlo Liashchynskyi P. Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS. arXiv. 2019. DOI: 10.48550/arXiv.1912.06059
39. Sun S., Shen J., Guo H., Zheng X., He R. Performance Evolution of High-Slump Concrete Under Vibration: Influence of Vibration Timing on Mechanical, Durability, and Interfacial Properties. Materials 2025. 18 (23). P. 5389. DOI: 10.3390/ma18235389
40. Liu J., Ye H., Yu K., Li H., Gan Z., Wang Y., Jiang Z., Zhang Z. Study on the Influence of Freeze–Thaw Cycles on the Shear Performance of the UHPC-NC Interface with Planted Reinforcement. Buildings. 2025. 15 (22). P. 4068. DOI: 10.3390/buildings15224068
41. Klyuev S., Klyuev A., Fediuk R., Ageeva M., Fomina E., Amran M., Murali G. Fresh and mechanical properties of low-cement mortars for 3D printing. Construction and Building Materials. 2022. 336 (12). P. 127644. DOI: 10.1016/j.conbuildmat.2022.127644
42. Klyuev S., Fediuk R., Ageeva M., Fomina E., Klyuev A., Shorstova E., Zolotareva S., Shchekina N., Shapovalova A., Sabitov L. Phase formation of mortar using technogenic fibrous materials. Case Studies in Construction Materials. 2022. 16 (2021). P. e01099. DOI: 10.1016/j.cscm.2022.e01099
43. Liu J., Guan D., Liu X. Comparative Performance Analysis of Machine Learning Models for Compressive Strength Prediction in Concrete Mix Design. Mathematical and Computational Applications. 2025. 30 (6). P. 128. DOI: 10.3390/mca30060128
44. Zhao Q., Yang Z., Zhang X., Xia Z., Xiong K., Yan J. An Experimental Study on the Mechanical Properties and ANN-Based Prediction of a Tensile Constitutive Model of ECCs. Polymers. 2025. 17 (23). P. 3183. DOI: 10.3390/polym17233183
2. Zhou J., Weng F., Liang Y., Liao Z., Zhang F., Fu M. Construction Control of Long-Span Combined Rail-Cum-Road Continuous Steel Truss Girder Bridge of High-Speed Railway. Buildings. 2025. 15 (22). P. 4204. DOI: 10.3390/buildings15224204
3. Ding X., Xu Y., Zheng M., Kang W., Xiahou X. Digital Technology Integration in Risk Management of Human–Robot Collaboration Within Intelligent Construction—A Systematic Review and Future Research Directions. Systems. 2025. 13 (11). P. 974. DOI: 10.3390/systems13110974
4. Kim C-W., Song T., Lee K., Yoo WS. A Framework for Evaluating Cost Performance of Architectural Projects Using Unstructured Data and Random Forest Model Focusing on Korean Cases. Buildings. 2025. 15 (20). P. 3799. DOI: 10.3390/buildings15203799
5. Filippova E., Hedayat S., Ziarati T., Manganelli M. Artificial Intelligence and Digital Twins for Bioclimatic Building Design: Innovations in Sustainability and Efficiency. Energies. 2025. 18 (19). P. 5230. DOI: 10.3390/en18195230
6. Yang Y., Chan A.P.C., Shan M., Gao R., Bao F., Lyu S., Zhang Q., Guan J. Opportunities and Challenges for Construction Health and Safety Technologies under the COVID-19 Pandemic in Chinese Construction Projects. Int. J. Environ. Res. Public Health. 2021. 18. P. 13038. DOI: 10.3390/ijerph182413038
7. Goh Y.M., Tian J., Chian E.Y.T. Management of safe distancing on construction sites during COVID-19: A smart real-time monitoring system. Computers & Industrial Engineering. 2022. 163. P. 107847. DOI: 10.1016/j.cie.2021.107847
8. Al-Khiami M.I., ElHadad M. Enhancing Construction Site Safety Using AI: The Development of a Custom Yolov8 Model for PPE Compliance Detection. Proceedings of the 2024 European Conference on Computing in Construction. Chania. Crete. Greece. 2024. DOI: 10.35490/EC3.2024.307
9. Rabbi A.B.K., Jeelani I. AI integration in construction safety: Current state, challenges, and future opportunities in text, vision, and audio based applications. Automation in Construction. 2024. 164. P. 105443. DOI: 10.1016/j.autcon.2024.105443.
10. Liu Z., Wang F., Wang W., Cao S., Gao X., Chen M. LSH-YOLO: A Lightweight Algorithm for Helmet-Wear Detection. Buildings. 2025. 15. P. 2918. DOI: 10.3390/buildings15162918
11. Tian K., Zhu Z., Mbachu J., Ghanbaripour A., Moorhead M. Artificial intelligence in risk management within the realm of construction projects: A bibliometric analysis and systematic literature review. Journal of Innovation & Knowledge. 2025. 10 (3). P. 100711. DOI: 10.1016/j.jik.2025.1007
12. Boamah F.A., Jin X, Senaratne S, Perera S. AI-driven risk identification model for infrastructure project: Utilising past project data. Expert Systems with Applications. 2025. 283. P. 127891. DOI: 10.1016/j.eswa.2025.127891
13. Yan A., Zhang S., Li Z., Zhu P., Wu Y. Prediction of Compressive Strength of Carbon Nanotube Reinforced Concrete Based on Multi-Dimensional Database. Buildings. 2025. 15 (23). P. 4349. DOI: 10.3390/buildings15234349
14. Huang P., Mei X., Sheng H., Li K., Di S., Cui Z. Prediction of Manufactured-Sand Concrete Compressive Strength Using Hybrid ML Models and Dream Optimization Algorithm. Mathematics. 2025. 13 (23). P. 3792. DOI: 10.3390/math13233792
15. Yan L., Liu P., Yao Y., Yang F., Feng X. Physics-Informed Multi-Task Neural Network (PINN) Learning for Ultra-High-Performance Concrete (UHPC) Strength Prediction. Buildings. 2025. 15 (23). P. 4243. DOI: 10.3390/buildings15234243
16. Olvera-Mayorga C.E., López-Martínez M.d.J., Rodríguez-Rodríguez J.A., Vázquez-Reyes S., Solís-Sánchez L.O., de la Rosa-Vargas J.I., Duarte-Correa D., González-Aviña J.V., Olvera-Olvera C.A. AI-Based Inference System for Concrete Compressive Strength: Multi-Dataset Analysis of Optimized Machine Learning Algorithms. Appl. Sci. 2025. 15 (23). P. 12383. DOI: 10.3390/app152312383
17. Rong H., Sun W., Ma H., Luo M., You Z., Zhang G., Zhu P., Liu Z., Gómez-Zamorano L.Y. Machine Learning-Driven Strength Prediction and Sustainability Analysis of Ultra-High-Performance Concrete. Materials. 2025. 18 (22). P. 5116. DOI: 10.3390/ma18225116
18. Fu H., Zhou X., Xu P., Sun D. Prediction of Compressive Strength of Concrete Using Explainable Machine Learning Models. Materials. 2025. 18 (21). P. 5009. DOI: 10.3390/ma1821500
19. Fediuk R., Amran M., Klyuev S., Klyuev A. Increasing the performance of a fiber-reinforced concrete for protective facilities. Fibers. 2021. 9 (11). P. 64. DOI: 10.3390/fib9110064
20. Hoque K.N., Presuel-Moreno F. Long-Term Corrosion Behavior of Reinforced Concrete: Impact of Supplementary Cementitious Materials and Reservoir Size Under Accelerated Chloride Ingress. Constr. Mater. 2025. 5 (2). P. 33. DOI: 10.3390/constrmater5020033
21. Lopez-Miguel A., Cabello-Mendez J.A., Moreno-Valdes A., Perez-Quiroz J.T., Machorro-Lopez J.M. Non-Destructive Testing of Concrete Materials from Piers: Evaluating Durability Through a Case Study. NDT. 2024. 2. P. 532-548. DOI: 10.3390/ndt2040033
22. Kim J.-S., Lee H.-Y., Kim J.-H.J. High-Performance Mortar with Epoxy-Coated Lightweight Aggregates for Marine Structures. Materials. 2025. 18 (18). P. 4257. DOI: 10.3390/ma18184257
23. Mazzú A.D.E., Dalfré G.M. Analysis of the Applicability of Accelerated Conditioning Protocols in Concrete Beams Reinforced with Steel and GFRP: Effects of Chloride Exposure. Polymers. 2025. 17 (17). P. 2423. DOI: 10.3390/polym17172423
24. Silva S.F.M.d., de Jesus W.S., de Almeida T.M.S., Novais R.Q.d.O., Sacramento L.A., Assis J.T.d., Anjos M.J.d., Pessôa J.R.d.C. Impact of Fiber Type on Chloride Ingress in Concrete: A MacroXRF Imaging Analysis. Applied Sciences. 2025. 15 (15). P. 8495. DOI: 10.3390/app15158495
25. Wang R., Qiao Z., Deng X., Shen X., Yang Y., Wang P., Zhang J. Experimental Investigation on Freeze–Thaw Durability of Polyacrylonitrile Fiber-Reinforced Recycled Concrete. Materials. 2025. 18 (7). P. 1548. DOI: 10.3390/ma18071548
26. Li W., Wang H., Liu Z., Li N., Zhao S., Hu S, Steel Slag Accelerated Carbonation Curing for High-Carbonation Precast Concrete Development. Materials. 2024. 17 (12). P. 2968. DOI: 10.3390/ma17122968
27. Gołaszewski J., Gołaszewska M., Cygan G. Performance of Ordinary and Self-Compacting Concrete with Limestone after Freeze–Thaw Cycles. Buildings. 2022. 12 (11). P. 2003. DOI: 10.3390/buildings12112003
28. Liang W., Liu S., Liu H., Yang G., Gao Y. Effect of Freeze–Thaw Cycles on Bond Properties at the FRP-Concrete Interface: Experimental Evaluation and Machine Learning Prediction. Buildings. 2025. 15 (22). P. 4038. DOI: 10.3390/buildings15224038
29. Mailyan L.R., Stel'makh S.A., Shcherban E.M., Zherebtsov Yu.V., Al-Tulaikhi M.M. Research of physicomechanical and design characteristics of vibrated, centrifuged and vibrocentrifuged concretes. Advanced Engineering Research. 2021. 21 (1). P. 5 – 13. DOI: 10.23947/2687-1653-2021-21-1-5-13
30. Mailyan L.R., Stel’makh S.A., Shcherban E.M. Differential characteristics of concrete in centrifugally spun and vibrospun building structures. Magazine of Civil Engineering. 2021. 108 (8). P. 10812. DOI: 10.34910/MCE.108.12
31. Kliukas R., Lukoševičienė O., Jaras A., Jonaitis B. The Mechanical Properties of Centrifuged Concrete in Reinforced Concrete Structures. Applied Sciences. 2020. 10 (10). P. 3570. DOI: 10.3390/app10103570
32. Feng B., Zhu Y.-H., Xie F., Chen J., Liu C.-B. Experimental Investigation and Design of Hollow Section, Centrifugal Concrete-Filled GFRP Tube Columns. Buildings. 2021. 11 (12). P. 598. DOI: 10.3390/buildings11120598
33. Stel’makh S.A., Shcherban’ E.M., Kholodnyak M.G., Nasevich A.S., Chernil’nik A.A. A device for manufacturing products from centrifuged concrete. Federation Patent 192492. 2019. https://patentimages.storage.googleapis.com/08/4b/f1/4688b6218e3156/RU192492U1.pdf.
34. Mailyan L.R., Stel’mak S.A., Shcherban’ E.M., Khalyushev A.K., Smolyanichenko A.S., Sysoev A.K., Parinov I.A., Cherpakov A.V. Investigation of Integral and Differential Characteristics of Variatropic Structure Heavy Concretes by Ultrasonic Methods. Applied Sciences. 2021. 11 (8). P. 3591. DOI: 10.3390/app11083591
35. Zubarev K.P., Razveeva I., Beskopylny A.N., Stel’makh S.A., Shcherban’ E.M., Mailyan L.R., Shakhalieva D.M., Chernil’nik A., Nikora N.I. Predicting the Strength of Heavy Concrete Exposed to Aggressive Environmental Influences by Machine Learning Methods. Buildings. 2025. 15 (21). P. 3998. DOI: 10.3390/buildings15213998
36. Beskopylny A.N., Stel’makh S.A., Shcherban’ E.M., Razveeva I., Kozhakin A., Meskhi B., Chernil’nik A., Elshaeva D., Ananova O., Girya M., Nurkhabinov T., Beskopylny N. Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete. Sensors. 2024. 24 (13). P. 4373. DOI: 10.3390/s24134373
37. Breiman L. Random Forests. Machine Learning. 2001. 45. P. 5 – 32. DOI: 10.1023/A:1010933404324
38. Liashchynskyi P, Pavlo Liashchynskyi P. Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS. arXiv. 2019. DOI: 10.48550/arXiv.1912.06059
39. Sun S., Shen J., Guo H., Zheng X., He R. Performance Evolution of High-Slump Concrete Under Vibration: Influence of Vibration Timing on Mechanical, Durability, and Interfacial Properties. Materials 2025. 18 (23). P. 5389. DOI: 10.3390/ma18235389
40. Liu J., Ye H., Yu K., Li H., Gan Z., Wang Y., Jiang Z., Zhang Z. Study on the Influence of Freeze–Thaw Cycles on the Shear Performance of the UHPC-NC Interface with Planted Reinforcement. Buildings. 2025. 15 (22). P. 4068. DOI: 10.3390/buildings15224068
41. Klyuev S., Klyuev A., Fediuk R., Ageeva M., Fomina E., Amran M., Murali G. Fresh and mechanical properties of low-cement mortars for 3D printing. Construction and Building Materials. 2022. 336 (12). P. 127644. DOI: 10.1016/j.conbuildmat.2022.127644
42. Klyuev S., Fediuk R., Ageeva M., Fomina E., Klyuev A., Shorstova E., Zolotareva S., Shchekina N., Shapovalova A., Sabitov L. Phase formation of mortar using technogenic fibrous materials. Case Studies in Construction Materials. 2022. 16 (2021). P. e01099. DOI: 10.1016/j.cscm.2022.e01099
43. Liu J., Guan D., Liu X. Comparative Performance Analysis of Machine Learning Models for Compressive Strength Prediction in Concrete Mix Design. Mathematical and Computational Applications. 2025. 30 (6). P. 128. DOI: 10.3390/mca30060128
44. Zhao Q., Yang Z., Zhang X., Xia Z., Xiong K., Yan J. An Experimental Study on the Mechanical Properties and ANN-Based Prediction of a Tensile Constitutive Model of ECCs. Polymers. 2025. 17 (23). P. 3183. DOI: 10.3390/polym17233183
Shcherban' E.M., Beskopylny A.N., Stel'makh S.A., Razveeva I.F., Mailyan L.R., Chernilnik A.A., Shakhalieva D.M., Beskopylny N.A. Variatropic concrete compressive strength prediction under freeze-thaw conditions using machine learning methods. Construction Materials and Products. 2025. 8 (6). 10. https://doi.org/10.58224/2618-7183-2025-8-6-10