Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model
DOI: https://doi.org/10.3846/jeelm.2025.23568Abstract
Concrete carbonation is a critical factor influencing the durability and longevity of concrete structures, particularly in urban environments. Traditional methods for predicting the anti-carbonation performance (ACP) of concrete often lack precision and fail to account for complex interactions between influencing factors. In this study, a novel hybrid model combining random forest (RF) regression with a least squares support vector machine (LSSVM) is proposed to enhance the accuracy of ACP predictions. The RF regression is utilized for feature selection, identifying the most significant factors affecting ACP and optimizing the input features for the LSSVM model. Our hybrid model is validated against a comprehensive dataset, demonstrating superior performance in predicting concrete carbonation resistance compared to conventional methods. Quantitative results show that the RF-LSSVM model achieves a root mean square error (RMSE) of 5e–5 and a high coefficient of determination (R-squared) of 0.999, indicating robust predictive capability and accuracy. The main novelty of this work lies in the integration of RF and LSSVM to create a robust, accurate, and practical tool for assessing the durability of concrete structures.
Keywords:
carbonation resistance, least squares support vector machine (LSSVM) model, safety assessment, random forest (RF), concreteHow to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abad, A., R. B., Ghorbani, H., Mohamadian, N., Davoodi, S., Mehrad, M., Khezerloo-ye Aghdam, S., & Nasriani, H. R. (2022). Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields. Fuel, 308, Article 121872. https://doi.org/10.1016/j.fuel.2021.121872> https://doi.org/10.1016/j.fuel.2021.121872
Al-Musawi, A. A., Alwanas, A. A. H., Salih, S. Q., Ali, Z. H., Tran, M. T., & Yaseen, Z. M. (2020). Shear strength of SFRCB without stirrups simulation: Implementation of hybrid artificial intelligence model. Engineering with Computers, 36(1), 1–11. https://doi.org/10.1007/s00366-018-0681-8> https://doi.org/10.1007/s00366-018-0681-8
Anemangely, M., Ramezanzadeh, A., Amiri, H., & Hoseinpour, S.-A. (2019). Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering, 174, 306–327. https://doi.org/10.1016/j.petrol.2018.11.032> https://doi.org/10.1016/j.petrol.2018.11.032
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., & Inman, D. J. (2021). A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications. Mechanical Systems and Signal Processing, 147, Article 107077. https://doi.org/10.1016/j.ymssp.2020.107077> https://doi.org/10.1016/j.ymssp.2020.107077
Barjouei, H. S., Ghorbani, H., & Mohamadian, N., Wood, D. A., Davoodi, S., Moghadasi, J., & Saberi, H. (2021). Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes. Journal of Petroleum Exploration and Production Technology, 11, 1233–1261. https://doi.org/10.1007/s13202-021-01087-4> https://doi.org/10.1007/s13202-021-01087-4
Chaabene, W. B., & Nehdi, M. L. (2020). Novel soft computing hybrid model for predicting shear strength and failure mode of SFRC beams with superior accuracy. Composites Part C: Open Access, 3, Article 100070. https://doi.org/10.1016/j.jcomc.2020.100070> https://doi.org/10.1016/j.jcomc.2020.100070
Chang, C. F., & Chen, J. W. (2006). The experimental investigation of concrete carbonation depth. Cement and Concrete Research, 36, 1760–1767. https://doi.org/10.1016/j.cemconres.2004.07.025> https://doi.org/10.1016/j.cemconres.2004.07.025
Chou, J. S., Pham, T. P. T., Nguyen, T. K., Pham, A. D., & Ngo, N. T. (2020). Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models. Soft Computing, 24(5), 3393–3411. https://doi.org/10.1007/s00500-019-04103-2> https://doi.org/10.1007/s00500-019-04103-2
Davoodi, S., Mehrad, M., Wood, D. A., Ghorbani, H., & Rukavishnikov, V. S. (2023a). Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids. Engineering Applications of Artificial Intelligence, 123, Article 106459. https://doi.org/10.1016/j.engappai.2023.106459> https://doi.org/10.1016/j.engappai.2023.106459
Davoodi, S., Mehrad, M., Wood, D. A., Rukavishnikov, V. S., & Bajolvand, M. (2023b). Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning. International Journal of Rock Mechanics and Mining Sciences, 170, Article 105546. https://doi.org/10.1016/j.ijrmms.2023.105546> https://doi.org/10.1016/j.ijrmms.2023.105546
Davoodi, S., Vo Thanh, H., Wood, D. A., Mehrad, M., & Rukavishnikov, V. S. (2023c). Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations. Applied Soft Computing, 143, Article 110408. https://doi.org/10.1016/j.asoc.2023.110408> https://doi.org/10.1016/j.asoc.2023.110408
Davoodi, S., Vo Thanh, H., Wood, D. A., Mehrad, M., Al-Shargabi, M., & Rukavishnikov, V. S. (2023d). Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables. Separation and Purification Technology, 316, Article 123807. https://doi.org/10.1016/j.seppur.2023.123807> https://doi.org/10.1016/j.seppur.2023.123807
Davoodi, S., Vo Thanh, H., Wood, D. A., Mehrad, M., Al-Shargabi, M., & Rukavishnikov, V. S. (2024). Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern – a theoretical study. Expert Systems with Applications, 250, Article 123944. https://doi.org/10.1016/j.eswa.2024.123944> https://doi.org/10.1016/j.eswa.2024.123944
Davoodi, S., Vo Thanh, H., Wood, D. A., Mehrad, M., Rukavishnikov, V. S., & Dai, Z. (2023e). Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites. Expert Systems with Applications, 222, Article 119796. https://doi.org/10.1016/j.eswa.2023.119796> https://doi.org/10.1016/j.eswa.2023.119796
Doddy, P., Cheng, M. Y., Wu, Y. W., & Duc-Hoc, T. (2020). Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Engineering with Computers, 36(3), 1135–1153.
España, R. M., Hernández-Díaz, A. M., Cecilia, J. M., & García-Román, M. D. (2017). Evolutionary strategies as applied to shear strain effects in reinforced concrete beams. Applied Soft Computing, 57, 164–176. https://doi.org/10.1016/j.asoc.2017.03.037> https://doi.org/10.1016/j.asoc.2017.03.037
Gandomi, A. H., Mohammadzadeh, S., Pérez-Ordóñez, J. L., & Alavi, A. H. (2014). Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups. Applied Soft Computing, 19, 112–120. https://doi.org/10.1016/j.asoc.2014.02.007> https://doi.org/10.1016/j.asoc.2014.02.007
Heoa, E. Y., Leeb, G., & Lee, G. (2016). Effect of elevated temperatures on chemical properties, microstructure and carbonation of cement paste. Journal of Ceramic Processing Research, 17(6), 648–652.
Ibrahim, A. M., Salman, W. D., & Bahlol, F. M. (2019). Flexural behavior of concrete composite beams with new steel tube section and different shear connectors. Tikrit Journal of Engineering Sciences, 26(1), 51–61. https://doi.org/10.25130/tjes.26.1.07> https://doi.org/10.25130/tjes.26.1.07
Jafarizadeh, F., Larki, B., Kazemi, B., Mehrad, M., Rashidi, S., Neycharan, J. G., Gandomgoun, M., & Gandomgoun, M. H. (2023). A new robust predictive model for lost circulation rate using convolutional neural network: A case study from Marun Oilfield. Petroleum, 9(3), 468–485. https://doi.org/10.1016/j.petlm.2022.04.002> https://doi.org/10.1016/j.petlm.2022.04.002
Jumaa’h, M., Kamil, B., & Baghabra, O. (2019). Mechanical and structural properties of a lightweight concrete with different types of recycling coarse aggregate. Tikrit Journal of Engineering Sciences, 26(1), 33–40. https://doi.org/10.25130/tjes.26.1.05> https://doi.org/10.25130/tjes.26.1.05
Junyoung, P., & Yootaek, K. (2014). Property enhancement of supercritically carbonated specimen by particle-size separation of fly ash and cement. Journal of Ceramic Processing Research, 15(4), 212–215.
Kamali, M. Z., Davoodi, S., Ghorbani, H., Wood, D. A., Mohamadian, N., Lajmorak, S., Rukavishnikov, V. S., Taherizade, F., & Band, S. S. (2022). Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling. Marine and Petroleum Geology, 139, Article 105597. https://doi.org/10.1016/j.marpetgeo.2022.105597> https://doi.org/10.1016/j.marpetgeo.2022.105597
Kar, S., Pandit, A. R., & Biswal, K. C. (2020). Prediction of FRP shear contribution for wrapped shear deficient RC beams using adaptive neuro-fuzzy inference system (ANFIS). Structures, 23, 702–717. https://doi.org/10.1016/j.istruc.2019.10.022> https://doi.org/10.1016/j.istruc.2019.10.022
Khalaf, J. A., Majeed, A. A., Aldlemy, M. S., Ali, Z. H., Zand, A. W. A., Adarsh, S., Bouaissi, A., Hameed, M. M., & Yaseen, Z. M. (2021). Hybridized deep learning model for perfobond rib shear strength connector prediction. Complexity, 2021, Article 6611885. https://doi.org/10.1155/2021/6611885> https://doi.org/10.1155/2021/6611885
Li, Y., Luo, Y., Du, H., Liu, W., Tang, L., & Xing, F. (2022). Evolution of microstructural characteristics of carbonated cement pastes subjected to high temperatures evaluated by MIP and SEM. Materials, 15(17), Article 6037. https://doi.org/10.3390/ma15176037> https://doi.org/10.3390/ma15176037
Ly, H. B., Le, T. T., Vu, H. L. T., Tran, V. Q., Le, L. M., & Pham, B. T. (2020). Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams. Sustainability, 12(7), 1–34. https://doi.org/10.3390/su12072709> https://doi.org/10.3390/su12072709
Mahmood, B. A., & Mohammad, K. I. (2019). Finite element analysis of RC deep beams under eccentric load. Tikrit Journal of Engineering Sciences, 26(1), 41–50. https://doi.org/10.25130/tjes.26.1.06> https://doi.org/10.25130/tjes.26.1.06
Maryam, S., Karzan, W., Majid, S., & Mahdi, S. (2018). Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. Smart Materials and Structures, 22, 413–424.
Matinkia, M., Amraeiniya, A., Behboud, M. M., Mehrad, M., Bajolvand, M., Gandomgoun, M. H., & Gandomgoun, M. (2022a). A novel approach to pore pressure modeling based on conventional well logs using convolutional neural network. Journal of Petroleum Science and Engineering, 211, Article 110156. https://doi.org/10.1016/j.petrol.2022.110156> https://doi.org/10.1016/j.petrol.2022.110156
Matinkia, M., Sheykhinasab, A., Shojaei, S., Vojdani Tazeh Kand, A., Elmi, A., Bajolvand, M., & Mehrad, M. (2022b). Developing a new model for drilling rate of penetration prediction using convolutional neural network. Arabian Journal for Science and Engineering, 47, 11953–11985. https://doi.org/10.1007/s13369-022-06765-x> https://doi.org/10.1007/s13369-022-06765-x
Mehrad, M., Bajolvand, M., & Ramezanzadeh, A., & Neycharan, J. G. (2020). Developing a new rigorous drilling rate prediction model using a machine learning technique. Journal of Petroleum Science and Engineering, 192, Article 107338. https://doi.org/10.1016/j.petrol.2020.107338> https://doi.org/10.1016/j.petrol.2020.107338
Mehrad, M., Ramezanzadeh, A., Bajolvand, M., & Hajsaeedi, M. R. (2022). Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms. Journal of Petroleum Science and Engineering, 212, Article 110254. https://doi.org/10.1016/j.petrol.2022.110254> https://doi.org/10.1016/j.petrol.2022.110254
Nafees, A., Javed, M. F., Khan, S., Nazir, K., Farooq, F., Aslam, F., Musarat, M. A., & Vatin, N. I. (2021). Predictive modeling of mechanical properties of silica fume-based green concrete using artificial intelligence approaches: MLPNN, ANFIS, and GEP. Materials, 14(24), Article 7531. https://doi.org/10.3390/ma14247531> https://doi.org/10.3390/ma14247531
Najafgholipour, M. A., Dehghan, S. M., Dooshabi, A., & Niroomandi, A. (2017). Finite element analysis of reinforced concrete beam-column connections with governing joint shear failure mode. Latin American Journal of Solids and Structures, 14(7), 1200–1225. https://doi.org/10.1590/1679-78253682> https://doi.org/10.1590/1679-78253682
Nguyen-Sy, T., Wakim, J., To, Q. D., Vu, M. N., Nguyen, T. D., & Nguyen, T. T. (2020). Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Construction and Building Materials, 260, Article 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757> https://doi.org/10.1016/j.conbuildmat.2020.119757
Penido, R. E. K., da Paixão, R. C. F., Costa, L. C. B., Peixoto, R. A. F., Cury, A. A., & Mendes, J. C. (2022). Predicting the compressive strength of steelmaking slag concrete with machine learning – considerations on developing a mix design tool. Construction and Building Materials, 341, Article 127896. https://doi.org/10.1016/j.conbuildmat.2022.127896> https://doi.org/10.1016/j.conbuildmat.2022.127896
Sharafati, A., Haghbin, M., Aldlemy, M. S., Mussa, M. H., Al Zand, A. W., Ali, M., Bhagat, S. K., Al-Ansari, N., & Yaseen, Z. M. (2020). Development of advanced computer aid model for shear strength of concrete slender beam prediction. Applied Sciences, 10(11), Article 3811. https://doi.org/10.3390/app10113811> https://doi.org/10.3390/app10113811
Sheykhinasab, A., Mohseni, A. A., & Barahooie Bahari, A., Naruei, E., Davoodi, S., Aghaz, A., & Mehrad, M. (2023). Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms. Journal of Petroleum Exploration and Production Technology, 13, 661–689. https://doi.org/10.1007/s13202-022-01593-z> https://doi.org/10.1007/s13202-022-01593-z
Sim, J., & Park, C. (2011). Compressive strength and resistance to chloride ion penetration and carbonation of recycled aggregate concrete with varying amount of fly ash and fine recycled aggregate. Waste Management, 31, 2352–2360. https://doi.org/10.1016/j.wasman.2011.06.014> https://doi.org/10.1016/j.wasman.2011.06.014
Solhmirzaei, R., Salehi, H., Kodur, V., & Naser, M. Z. (2020). Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams. Engineering Structures, 224, Article 111221. https://doi.org/10.1016/j.engstruct.2020.111221> https://doi.org/10.1016/j.engstruct.2020.111221
Talkhouncheh, M. Z., Davoodi, S., Larki, B., Mehrad, M., Rashidi, S., & Vasfi, M. (2023). A new approach to mechanical brittleness index modeling based on conventional well logs using hybrid algorithms. Earth Science Informatics, 16, 3387–3416. https://doi.org/10.1007/s12145-023-01098-1> https://doi.org/10.1007/s12145-023-01098-1
Talkhouncheh, M. Z., Davoodi, S., Wood, D. A., Mehrad, M., Rukavishnikov, V. S., & Bakhshi, R. (2024). Robust machine learning predictive models for real-time determination of confined compressive strength of rock using mudlogging data. Rock Mechanics and Rock Engineering, 57, 6881–6907. https://doi.org/10.1007/s00603-024-03859-w> https://doi.org/10.1007/s00603-024-03859-w
Vasanthalin, P. C., & Kavitha, N. C. (2021). Prediction of compressive strength of recycled aggregate concrete using artificial neural network and cuckoo search method. Materials Today: Proceedings, 46, 8480–8488. https://doi.org/10.1016/j.matpr.2021.03.500> https://doi.org/10.1016/j.matpr.2021.03.500
Verapathran, M., Vivek, S., Arunkumar, G. E., & Dhavashankaran, D. (2023). Flexural behaviour of HPC beams with steel slag aggregate. Journal of Ceramic Processing Research, 24(1), 89–97.
Wu, J., Xu, J., Diao, B., & Panesar, D. K. (2021). Impacts of reinforcement ratio and fatigue load level on the chloride ingress and service life estimating of fatigue loaded reinforced concrete (RC) beams. Construction and Building Materials, 266, Article 120999. https://doi.org/10.1016/j.conbuildmat.2020.120999> https://doi.org/10.1016/j.conbuildmat.2020.120999
Yaseen, Z. M., Tran, M. T., Kim, S., Bakhshpoori, T., & Deo, R. C. (2018). Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach. Engineering Structures, 177, 244–255. https://doi.org/10.1016/j.engstruct.2018.09.074> https://doi.org/10.1016/j.engstruct.2018.09.074
Zeng, Z., Zhu, Z., Yao, W., Wang, Z., Wang, C., Wei, Y., Wei, Z., & Guan, X. (2022). Accurate prediction of concrete compressive strength based on explainable features using deep learning. Construction and Building Materials, 329, Article 127082. https://doi.org/10.1016/j.conbuildmat.2022.127082> https://doi.org/10.1016/j.conbuildmat.2022.127082
Zhang, G., Ali, Z. H., Aldlemy, M. S., Mussa, M. H., Salih, S. Q., Hameed, M. M., Al-Khafaji, Z. S., & Yaseen, Z. M. (2022). Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model. Engineering with Computers, 38(Suppl. 1), 15–28. https://doi.org/10.1007/s00366-020-01137-1> https://doi.org/10.1007/s00366-020-01137-1
Zhao, Z., Qu, X., & Li, J. (2020). Application of polymer modified cementitious coatings (PCCs) for impermeability enhancement of concrete. Construction and Building Materials, 249, Article 118769. https://doi.org/10.1016/j.conbuildmat.2020.118769> https://doi.org/10.1016/j.conbuildmat.2020.118769
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.