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English
The article presents a study of the application of artificial intelligence algorithms in predicting the risk of early cracking in massive reinforced concrete structures using monolithic foundation slabs as an example. The current experience of using algorithms such as convolutional neural networks, deep learning tools (YOLOv5 model) for crack detection at various stages of the life cycle of massive reinforced concrete structures is analyzed. The causes of crack formation, physical and mechanical processes, including cement hydration are considered.
A model has been developed that predicts the magnitude of the tensile stress level in monolithic foundation slabs during construction, based on CatBoost using Python, allowing to predict the risks of early cracking with an accuracy of up to 98%.
The model was trained on synthetic data containing various design parameters and material properties, including the geometric dimensions of the slabs, the temperature on the upper surface, the heat transfer coefficient on the upper surface, the curing rate, the class of concrete and the characteristics of the soil base. Statistical analysis of the data was performed, a correlation matrix was constructed. Practical and predicted values of the model were visualized in the form of a scatter plot. The most significant parameters influencing the risk of early cracking in massive monolithic foundation slabs were obtained. The constructed model passed quality assessment according to three metrics: MAE=0.0011; MSE=4.038; MAPE=0.0014.
A model has been developed that predicts the magnitude of the tensile stress level in monolithic foundation slabs during construction, based on CatBoost using Python, allowing to predict the risks of early cracking with an accuracy of up to 98%.
The model was trained on synthetic data containing various design parameters and material properties, including the geometric dimensions of the slabs, the temperature on the upper surface, the heat transfer coefficient on the upper surface, the curing rate, the class of concrete and the characteristics of the soil base. Statistical analysis of the data was performed, a correlation matrix was constructed. Practical and predicted values of the model were visualized in the form of a scatter plot. The most significant parameters influencing the risk of early cracking in massive monolithic foundation slabs were obtained. The constructed model passed quality assessment according to three metrics: MAE=0.0011; MSE=4.038; MAPE=0.0014.
[1] Smolana A., Klemczak B., Azenha M., Schlicke D. Early age cracking risk in a massive concrete foundation slab: Comparison of analytical and numerical prediction models with on-site measurements. Construction and Building Materials. 2021. 301. Article 124135. https://doi.org/10.1016/j.conbuildmat.2021.124135
[2] Luu V.T., Le T., Nguyen H. Research on thermal cracking control in mass concrete by using cooling pile system. Journal of Science and Technology in Civil Engineering. 2019. 13. P. 99 – 107.
[3] Nguyen C., Ho K., Tran H. The mathematical prediction model for temperature regime in the mass concrete block using the cooling pipe system. Journal of Science and Technology in Civil Engineering. 2020. 14. P. 27 – 38.
[4] Do D., Nguyen C., Lam K. The effect of concrete block size on the formation of temperature field and cracking of an early age. Vietnam Journal of Construction. 2020. 620. P. 11 – 14.
[5] Klemczak B., Smolana A. Multi-Step Procedure for Predicting Early-Age Thermal Cracking Risk in Mass Concrete Structures. Materials. 2024. 17 (15). Article 3700. https://doi.org/10.3390/ma17153700
[6] Kim J.J., Kim A.R., Lee S.W. Artificial neural network-based crack automated detection and analysis for the inspection of concrete structures. Applied Sciences. 2020. 10 (22). Article 8105. https://doi.org/10.3390/app10228105
[7] Beskopylny A. N., Stel’makh S.A., Shcherban’ E.M., Razveeva I., Kozhakin A., Meskhi B., Beskopylny N. et. al. Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete. Sensors. 2024. 24. Article 4373. https://doi.org/10.3390/s24134373
[8] Beskopylny A.N., Stel’makh S.A., Shcherban’ E.M., Razveeva I., Kozhakin A., Pembek A., Beskopylny N. et al. Prediction of the Properties of Vibro -Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods. Buildings. 2024. 14. Article 1198. https://doi.org/10.3390/buildings14051198
[9] Beskopylny A.N., Stel’makh S A., Shcherban’ E.M., Mailyan L.R., Meskhi B., Razveeva I., Beskopylny N. et al. Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods. Buildings. 2024. 14. Article 377. https://doi.org/10.3390/buildings14020377
[10] Yu Y., Rashidi M., Samali B., Mohammadi M., Nguyen T. N., Zhou X. Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm. Structural Health Monitoring. 2022. 21 (5). P. 2244 – 2263. https://doi.org/10.1177/14759217211053546
[11] Golewski G.L. The phenomenon of cracking in cement concretes and reinforced concrete structures: the mechanism of cracks formation, causes of their initiation, types and places of occurrence, and methods of detection – a review. Buildings. 2023. 13 (3). Article 765. https://doi.org/10.3390/buildings13030765
[12] Beskopylny A.N., Shcherban’ E.M., Stel’makh S.A., Mailyan L.R., Meskhi B., Razveeva I., Onore G. et al. Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network. Applied Sciences. 2023. 13. Article 1904. https://doi.org/10.3390/app13031904
[13] Zhang G., Ali Z.H., Aldlemy M. S., Mussa M.H., Salih S.Q., Hameed M.M., Yaseen Z.M. et al. Reinforced concrete deep beam shear strength capacity modeling using an integrative bio-inspired algorithm with an artificial intelligence model. Engineering with Computers. 2022. 38. P. 15 – 28. https://doi.org/ 10.1007/S00366-020-01137-1
[14] Tapeh A.T.G., Naser M.Z. Artificial intelligence, machine learning, and deep learning in structural engineering: a scientificometrics review of trends and best practices. Archives of Computational Methods in Engineering. 2023. 30 (1). P. 115 – 159. https://doi.org/10.1007/s11831-022-09793-w
[15] Athanasiou A., Ebrahimkhanlou A., Zaborac J., Hrynyk T., Salamone S. A machine learning approach based on multifractal features for crack assessment of reinforced concrete shells. Computer-Aided Civil and Infrastructure Engineering. 2020. 35 (6). P. 565 – 578. https://doi.org/10.1111/mice.12509
[16] Fan W., Chen Y., Li J., Sun Y., Feng J., Hassanin H., Sareh P. Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications. Structures. 2021. 33. P. 3954 – 3963. https://doi.org/10.1016/j.istruc.2021.06.110
[17] Hu X., Li B., Mo Y., Alselwi O. Progress in artificial intelligence-based prediction of concrete performance. Journal of Advanced Concrete Technology. 2021. 19 (8). P. 924 – 936. https://doi.org/ 10.3151/jact.19.924
[18] Xu G., Yue Q., Liu X. Deep learning algorithm for real-time automatic crack detection, segmentation, qualification. Engineering Applications of Artificial Intelligence. 2023. 126. Article 107085. https://doi.org/10.1016/j.engappai.2023.107085
[19] Jin S., Lee S.E., Hong J.W. A vision-based approach for autonomous crack width measurement with flexible kernel. Automation in Construction. 2020. 110. Article 103019. https://doi.org/10.1016/j.autcon.2019.103019
[20] Laxman K. C., Tabassum N., Ai L., Cole C., Ziehl P. Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning. Construction and Building Materials. 2023. 370. Article 130709. https://doi.org/10.1016/j.conbuildmat.2023.130709
[21] Li R., Yu J., Li F., Yang R., Wang Y., Peng Z. Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN. Construction and Building Materials. 2023. 362. Article 129659. https://doi.org/10.1016/j.conbuildmat.2022.129659
[22] Chepurnenko A., Turina V., Akopyan V. Artificial Neural Network Models for Predicting the Early Cracking Risk in Massive Monolithic Foundation Slabs. The Open Civil Enginnering Journal. 2024. 18. Article e18741495358647. https://doi.org/10.2174/0118741495358647241024110350
[23] Nesvetaev G.V., Koryanova Yu. I., Shut V.V. Specific heat dissipation of concrete and the risk of early cracking of massive reinforced concrete foundation slabs. Construction Materials and Products. 2024. 7 (4). 3. https://doi.org/10.58224/2618-7183-2024-7-4-3
[24] Nesvetaev G.V., Koryanova Y.I., Chepurnenko A.S., Sukhin D.P. On the issue of modeling thermal stresses during concreting of massive reinforced concrete slabs. Engineering Journal of Don. 2022. 6. P. 375 – 394. URL: http://www.ivdon.ru/en/magazine/archive/n6y2022/7691 (accessed on 05 November 2024)
[25] Chepurnenko A., Nesvetaev G., Koryanova Y. Modeling non-stationary temperature fields when constructing mass cast-in-situ reinforced-concrete foundation slabs. Architecture and Engineering. 2022. 7 (2). P. 66 – 78. https://doi.org/10.23968/2500-0055-2022-7-2-66-78
[26] Chepurnenko A., Nesvetaev G., Koryanova Y., Yazyev B. Simplified model for determining the stress-strain state in massive monolithic foundation slabs during construction. International Journal for Computational Civil and Structural Engineering. 2022. 18 (3). P. 126 – 136. https://doi.org/10.22337/2587-9618-2022-18-3-126-136
[27] Mordovsky S.S. Initial modulus of elasticity of concrete and methods for its determination. In Traditions and innovations in construction and architecture. Construction and construction technologies. 2022. P. 37 – 45. URL: https://elibrary.ru/item.asp?id = 49012815 (accessed on 05 November 2024)
[28] Bjøntegaard Ø. Basis for and practical approaches to stress calculations and crack risk estimation in hardening concrete structures – State of the art FA 3 Technical performance. SP 3.1 Crack free concrete structures. 2011. URL: https://sintef.brage.unit.no/sintef-xmlui/bitstream/handle/11250/2411102/coin31.pdf (accessed on 13 November 2024)
[29] Smolana A., Klemczak B., Azenha M., Schlicke D. Thermo-mechanical analysis of mass concrete foundation slabs at early age – essential aspects and experiences from the FE modelling. Materials. 2022. 15 (5). Article 1815. https://doi.org/10.3390/ma15051815
[30] Kondratieva T.N., Chepurnenko A.S. Prediction of Rheological Parameters of Polymers by Machine Learning Methods. Advanced Engineering Research (Rostov-on-Don). 2024. 24 (1). P. 36 – 47. https://doi.org/10.23947/2687-1653-2024-24-1-36-47.
[31] Stel'makh S.A., Beskopylny A.N., Shcherban E.M., Mavzolevsky D., Drukarenko S., Chernil’nik A., Shilov, A.A. Influence of Corn Cob Ash Additive on the Structure and Properties of Cement Concrete. Construction Materials and Products. 2024. 7 (3). P. 2. https://doi.org/10.58224/2618-7183-2024-7-3-2
[2] Luu V.T., Le T., Nguyen H. Research on thermal cracking control in mass concrete by using cooling pile system. Journal of Science and Technology in Civil Engineering. 2019. 13. P. 99 – 107.
[3] Nguyen C., Ho K., Tran H. The mathematical prediction model for temperature regime in the mass concrete block using the cooling pipe system. Journal of Science and Technology in Civil Engineering. 2020. 14. P. 27 – 38.
[4] Do D., Nguyen C., Lam K. The effect of concrete block size on the formation of temperature field and cracking of an early age. Vietnam Journal of Construction. 2020. 620. P. 11 – 14.
[5] Klemczak B., Smolana A. Multi-Step Procedure for Predicting Early-Age Thermal Cracking Risk in Mass Concrete Structures. Materials. 2024. 17 (15). Article 3700. https://doi.org/10.3390/ma17153700
[6] Kim J.J., Kim A.R., Lee S.W. Artificial neural network-based crack automated detection and analysis for the inspection of concrete structures. Applied Sciences. 2020. 10 (22). Article 8105. https://doi.org/10.3390/app10228105
[7] Beskopylny A. N., Stel’makh S.A., Shcherban’ E.M., Razveeva I., Kozhakin A., Meskhi B., Beskopylny N. et. al. Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete. Sensors. 2024. 24. Article 4373. https://doi.org/10.3390/s24134373
[8] Beskopylny A.N., Stel’makh S.A., Shcherban’ E.M., Razveeva I., Kozhakin A., Pembek A., Beskopylny N. et al. Prediction of the Properties of Vibro -Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods. Buildings. 2024. 14. Article 1198. https://doi.org/10.3390/buildings14051198
[9] Beskopylny A.N., Stel’makh S A., Shcherban’ E.M., Mailyan L.R., Meskhi B., Razveeva I., Beskopylny N. et al. Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods. Buildings. 2024. 14. Article 377. https://doi.org/10.3390/buildings14020377
[10] Yu Y., Rashidi M., Samali B., Mohammadi M., Nguyen T. N., Zhou X. Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm. Structural Health Monitoring. 2022. 21 (5). P. 2244 – 2263. https://doi.org/10.1177/14759217211053546
[11] Golewski G.L. The phenomenon of cracking in cement concretes and reinforced concrete structures: the mechanism of cracks formation, causes of their initiation, types and places of occurrence, and methods of detection – a review. Buildings. 2023. 13 (3). Article 765. https://doi.org/10.3390/buildings13030765
[12] Beskopylny A.N., Shcherban’ E.M., Stel’makh S.A., Mailyan L.R., Meskhi B., Razveeva I., Onore G. et al. Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network. Applied Sciences. 2023. 13. Article 1904. https://doi.org/10.3390/app13031904
[13] Zhang G., Ali Z.H., Aldlemy M. S., Mussa M.H., Salih S.Q., Hameed M.M., Yaseen Z.M. et al. Reinforced concrete deep beam shear strength capacity modeling using an integrative bio-inspired algorithm with an artificial intelligence model. Engineering with Computers. 2022. 38. P. 15 – 28. https://doi.org/ 10.1007/S00366-020-01137-1
[14] Tapeh A.T.G., Naser M.Z. Artificial intelligence, machine learning, and deep learning in structural engineering: a scientificometrics review of trends and best practices. Archives of Computational Methods in Engineering. 2023. 30 (1). P. 115 – 159. https://doi.org/10.1007/s11831-022-09793-w
[15] Athanasiou A., Ebrahimkhanlou A., Zaborac J., Hrynyk T., Salamone S. A machine learning approach based on multifractal features for crack assessment of reinforced concrete shells. Computer-Aided Civil and Infrastructure Engineering. 2020. 35 (6). P. 565 – 578. https://doi.org/10.1111/mice.12509
[16] Fan W., Chen Y., Li J., Sun Y., Feng J., Hassanin H., Sareh P. Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications. Structures. 2021. 33. P. 3954 – 3963. https://doi.org/10.1016/j.istruc.2021.06.110
[17] Hu X., Li B., Mo Y., Alselwi O. Progress in artificial intelligence-based prediction of concrete performance. Journal of Advanced Concrete Technology. 2021. 19 (8). P. 924 – 936. https://doi.org/ 10.3151/jact.19.924
[18] Xu G., Yue Q., Liu X. Deep learning algorithm for real-time automatic crack detection, segmentation, qualification. Engineering Applications of Artificial Intelligence. 2023. 126. Article 107085. https://doi.org/10.1016/j.engappai.2023.107085
[19] Jin S., Lee S.E., Hong J.W. A vision-based approach for autonomous crack width measurement with flexible kernel. Automation in Construction. 2020. 110. Article 103019. https://doi.org/10.1016/j.autcon.2019.103019
[20] Laxman K. C., Tabassum N., Ai L., Cole C., Ziehl P. Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning. Construction and Building Materials. 2023. 370. Article 130709. https://doi.org/10.1016/j.conbuildmat.2023.130709
[21] Li R., Yu J., Li F., Yang R., Wang Y., Peng Z. Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN. Construction and Building Materials. 2023. 362. Article 129659. https://doi.org/10.1016/j.conbuildmat.2022.129659
[22] Chepurnenko A., Turina V., Akopyan V. Artificial Neural Network Models for Predicting the Early Cracking Risk in Massive Monolithic Foundation Slabs. The Open Civil Enginnering Journal. 2024. 18. Article e18741495358647. https://doi.org/10.2174/0118741495358647241024110350
[23] Nesvetaev G.V., Koryanova Yu. I., Shut V.V. Specific heat dissipation of concrete and the risk of early cracking of massive reinforced concrete foundation slabs. Construction Materials and Products. 2024. 7 (4). 3. https://doi.org/10.58224/2618-7183-2024-7-4-3
[24] Nesvetaev G.V., Koryanova Y.I., Chepurnenko A.S., Sukhin D.P. On the issue of modeling thermal stresses during concreting of massive reinforced concrete slabs. Engineering Journal of Don. 2022. 6. P. 375 – 394. URL: http://www.ivdon.ru/en/magazine/archive/n6y2022/7691 (accessed on 05 November 2024)
[25] Chepurnenko A., Nesvetaev G., Koryanova Y. Modeling non-stationary temperature fields when constructing mass cast-in-situ reinforced-concrete foundation slabs. Architecture and Engineering. 2022. 7 (2). P. 66 – 78. https://doi.org/10.23968/2500-0055-2022-7-2-66-78
[26] Chepurnenko A., Nesvetaev G., Koryanova Y., Yazyev B. Simplified model for determining the stress-strain state in massive monolithic foundation slabs during construction. International Journal for Computational Civil and Structural Engineering. 2022. 18 (3). P. 126 – 136. https://doi.org/10.22337/2587-9618-2022-18-3-126-136
[27] Mordovsky S.S. Initial modulus of elasticity of concrete and methods for its determination. In Traditions and innovations in construction and architecture. Construction and construction technologies. 2022. P. 37 – 45. URL: https://elibrary.ru/item.asp?id = 49012815 (accessed on 05 November 2024)
[28] Bjøntegaard Ø. Basis for and practical approaches to stress calculations and crack risk estimation in hardening concrete structures – State of the art FA 3 Technical performance. SP 3.1 Crack free concrete structures. 2011. URL: https://sintef.brage.unit.no/sintef-xmlui/bitstream/handle/11250/2411102/coin31.pdf (accessed on 13 November 2024)
[29] Smolana A., Klemczak B., Azenha M., Schlicke D. Thermo-mechanical analysis of mass concrete foundation slabs at early age – essential aspects and experiences from the FE modelling. Materials. 2022. 15 (5). Article 1815. https://doi.org/10.3390/ma15051815
[30] Kondratieva T.N., Chepurnenko A.S. Prediction of Rheological Parameters of Polymers by Machine Learning Methods. Advanced Engineering Research (Rostov-on-Don). 2024. 24 (1). P. 36 – 47. https://doi.org/10.23947/2687-1653-2024-24-1-36-47.
[31] Stel'makh S.A., Beskopylny A.N., Shcherban E.M., Mavzolevsky D., Drukarenko S., Chernil’nik A., Shilov, A.A. Influence of Corn Cob Ash Additive on the Structure and Properties of Cement Concrete. Construction Materials and Products. 2024. 7 (3). P. 2. https://doi.org/10.58224/2618-7183-2024-7-3-2
Kondratieva T.N., Tyurina V.S., Chepurnenko A.S. Predicting the risk of early cracking in massive monolithic foundation slabs using artificial intelligence algorithms. Construction Materials and Products. 2025. 8 (1). 6. https://doi.org/10.58224/2618-7183-2025-8-1-6