Predicting the risk of early cracking in massive monolithic foundation slabs using artificial intelligence algorithms

Cover Page

Cite item

Full Text

Abstract

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.

About the authors

T. N Kondratieva

Don State Technical University

ORCID iD: 0000-0002-3518-8942

V. S Tyurina

Don State Technical University

ORCID iD: 0009-0001-6399-401X

A. S Chepurnenko

Don State Technical University

ORCID iD: 0000-0002-9133-8546

References

  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

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).