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A hybrid artificial intelligence approach for modeling the carbonation depth of sustainable concrete containing fly ash


  • Bertolini, L., Elsener, B., Pedeferri, P., Redaelli, E. & Polder, R. B. Corrosion of Steel in Concrete: Prevention, Diagnosis, Repair (Wiley, 2013).


    Google Scholar
     

  • Czarnecki, L. & Woyciechowski, P. Modelling of concrete carbonation; Is it a process unlimited in time and restricted in space?. Bull. Pol. Acad. Sci. Tech. Sci. https://doi.org/10.1515/bpasts-2015-0006 (2015).

    Article 

    Google Scholar
     

  • Hulimka, J. & Kałuża, M. Basic chemical tests of concrete during the assessment of structure suitability—Discussion on selected industrial structures. Appl. Sci. 10(1), 358 (2020).

    CAS 

    Google Scholar
     

  • Martys, N. S. & Ferraris, C. F. Capillary transport in mortars and concrete. Cem. Concr. Res. 27(5), 747–760 (1997).

    CAS 

    Google Scholar
     

  • Carević, V., Ignjatović, I. & Dragaš, J. Model for practical carbonation depth prediction for high volume fly ash concrete and recycled aggregate concrete. Constr. Build. Mater. 213, 194–208 (2019).


    Google Scholar
     

  • Castellote, M. & Andrade, C. Modelling the carbonation of cementitious matrixes by means of the unreacted-core model, UR-CORE. Cem. Concr. Res. 38(12), 1374–1384 (2008).

    CAS 

    Google Scholar
     

  • Cui, H., Tang, W., Liu, W., Dong, Z. & Xing, F. Experimental study on effects of CO2 concentrations on concrete carbonation and diffusion mechanisms. Constr. Build. Mater. 93, 522–527 (2015).


    Google Scholar
     

  • Leemann, A. & Moro, F. Carbonation of concrete: The role of CO2 concentration, relative humidity and CO2 buffer capacity. Mater. Struct. 50, 1–14 (2017).


    Google Scholar
     

  • Elsalamawy, M., Mohamed, A. R. & Kamal, E. M. The role of relative humidity and cement type on carbonation resistance of concrete. Alex. Eng. J. 58(4), 1257–1264 (2019).


    Google Scholar
     

  • Williams, P. J. et al. Microanalysis of alkali-activated fly ash–CH pastes. Cem. Concr. Res. 32(6), 963–972 (2002).

    CAS 

    Google Scholar
     

  • McCarthy, M. & Dhir, R. Development of high volume fly ash cements for use in concrete construction. Fuel 84(11), 1423–1432 (2005).

    CAS 

    Google Scholar
     

  • Atiş, C. D. Accelerated carbonation and testing of concrete made with fly ash. Constr. Build. Mater. 17(3), 147–152 (2003).


    Google Scholar
     

  • Khunthongkeaw, J., Tangtermsirikul, S. & Leelawat, T. A study on carbonation depth prediction for fly ash concrete. Constr. Build. Mater. 20(9), 744–753 (2006).


    Google Scholar
     

  • Thomas, M. & Matthews, J. Carbonation of fly ash concrete. Mag. Concr. Res. 44(160), 217–228 (1992).

    CAS 

    Google Scholar
     

  • Possan, E., Andrade, J., Dal Molin, D. & Ribeiro, J. L. D. Model to estimate concrete carbonation depth and service life prediction. In Hygrothermal Behaviour and Building Pathologies 67–97 (Springer, 2021).


    Google Scholar
     

  • Ta, V.-L., Bonnet, S., Kiesse, T. S. & Ventura, A. A new meta-model to calculate carbonation front depth within concrete structures. Constr. Build. Mater. 129, 172–181 (2016).

    CAS 

    Google Scholar
     

  • Liu, P., Yu, Z. & Chen, Y. Carbonation depth model and carbonated acceleration rate of concrete under different environment. Cem. Concr. Compos. 114, 103736 (2020).

    CAS 

    Google Scholar
     

  • Li, Q. et al. Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques. Sci. Rep. 13(1), 20102 (2023).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kazemi, R. Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend. Eng. Rep. 5, e12676 (2023).


    Google Scholar
     

  • Wu, F., Tang, F., Lu, R. & Cheng, M. Predicting compressive strength of RCFST columns under different loading scenarios using machine learning optimization. Sci. Rep. 13(1), 16571 (2023).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ashrafian, A., Panahi, E., Salehi, S. & Amiri, M. J. T. On the implementation of the interpretable data-intelligence model for designing service life of structural concrete in a marine environment. Ocean Eng. 256, 111523 (2022).


    Google Scholar
     

  • Ashrafian, A., Behnood, A., Golafshani, E. M., Panahi, E. & Berenjian, J. Toward presenting an ensemble meta-model for evaluation of pozzolanic mixtures incorporating industrial by-products. Struct. Concr. https://doi.org/10.1002/suco.202300452 (2023).

    Article 

    Google Scholar
     

  • Ashrafian, A., Hamzehkolaei, N. S., Dwijendra, N. K. A. & Yazdani, M. An evolutionary neuro-fuzzy-based approach to estimate the compressive strength of eco-friendly concrete containing recycled construction wastes. Buildings 12(8), 1280 (2022).


    Google Scholar
     

  • Ashrafian, A., Panahi, E., Salehi, S., Karoglou, M. & Asteris, P. G. Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques. In Structures (Elsevier, 2023).


    Google Scholar
     

  • Parhi, S. K., Dwibedy, S. & Panigrahi, S. K. AI-driven critical parameter optimization of sustainable self-compacting geopolymer concrete. J. Build. Eng. https://doi.org/10.1016/j.jobe.2024.108923 (2024).

    Article 

    Google Scholar
     

  • Parhi, S. K., Panda, S., Dwibedy, S. & Panigrahi, S. K. Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete. Multiscale Multidiscip. Model. Exp. Design 1, 28. https://doi.org/10.1007/s41939-023-00349-4 (2024).

    Article 

    Google Scholar
     

  • Rafiei, M. H., Khushefati, W. H., Demirboga, R. & Adeli, H. Neural network, machine learning, and evolutionary approaches for concrete material characterization. ACI Mater. J. 113(6), 781–789 (2016).


    Google Scholar
     

  • Basheer, I. A. & Hajmeer, M. Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3–31 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • Kazemi, R., Shadnia, R., Eskandari-Naddaf, H. & Zhang, L. The properties of cement-mortar at different cement strength classes: Experimental study and multi-objective modeling. Arab. J. Sci. Eng. 47(10), 13381–13396 (2022).

    CAS 

    Google Scholar
     

  • Kazemi, R. & Naser, M. Towards sustainable use of foundry by-products: Evaluating the compressive strength of green concrete containing waste foundry sand using hybrid biogeography-based optimization with artificial neural networks. J. Build. Eng. 76, 107252 (2023).


    Google Scholar
     

  • Korouzhdeh, T., Eskandari-Naddaf, H. & Kazemi, R. Hybrid artificial neural network with biogeography-based optimization to assess the role of cement fineness on ecological footprint and mechanical properties of cement mortar expose to freezing/thawing. Constr. Build. Mater. 304, 124589 (2021).


    Google Scholar
     

  • Kazemi, R. & Gholampour, A. Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence framework. Constr. Build. Mater. 409, 133835 (2023).

    CAS 

    Google Scholar
     

  • Parhi, S. K. & Panigrahi, S. K. Alkali–silica reaction expansion prediction in concrete using hybrid metaheuristic optimized machine learning algorithms. Asian J. Civ. Eng. 25(1), 1091–1113 (2024).


    Google Scholar
     

  • Kazemi, R., Eskandari-Naddaf, H. & Korouzhdeh, T. New insight into the prediction of strength properties of cementitious mortar containing nano-and micro-silica based on porosity using hybrid artificial intelligence techniques. Struct. Concr. https://doi.org/10.1002/suco.202200101 (2023).

    Article 

    Google Scholar
     

  • Felix, E. F., Carrazedo, R. & Possan, E. Carbonation model for fly ash concrete based on artificial neural network: Development and parametric analysis. Constr. Build. Mater. 266, 121050 (2021).

    CAS 

    Google Scholar
     

  • Kellouche, Y., Boukhatem, B., Ghrici, M. & Tagnit-Hamou, A. Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network. Neural Comput. Appl. 31(2), 969–988 (2019).


    Google Scholar
     

  • Tran, V. Q., Mai, H. V. T., To, Q. T. & Nguyen, M. H. Machine learning approach in investigating carbonation depth of concrete containing Fly ash. Struct. Concr. 24(2), 2145–2169 (2023).


    Google Scholar
     

  • Huo, Z., Wang, L. & Huang, Y. Predicting carbonation depth of concrete using a hybrid ensemble model. J. Build. Eng. 76, 107320 (2023).


    Google Scholar
     

  • Haykin, S. Neural Networks and Learning Machines, 3/E (Pearson Education India, 2009).


    Google Scholar
     

  • Lippmann, R. An introduction to computing with neural nets. IEEE Assp Mag. 4(2), 4–22 (1987).


    Google Scholar
     

  • Simon, D. Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008).


    Google Scholar
     

  • Mirjalili, S., Mirjalili, S. M. & Lewis, A. Let a biogeography-based optimizer train your multi-layer perceptron. Inf. Sci. 269, 188–209 (2014).

    MathSciNet 

    Google Scholar
     

  • Kaveh, M., Khishe, M. & Mosavi, M. Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network. Analog Integr. Circ. Signal Process. 100, 405–428 (2019).


    Google Scholar
     

  • Ma, H., Simon, D., Siarry, P., Yang, Z. & Fei, M. Biogeography-based optimization: A 10-year review. IEEE Trans. Emerg. Top. Comput. Intell. 1(5), 391–407 (2017).


    Google Scholar
     

  • Ma, H. & Simon, D. Evolutionary Computation with Biogeography-based Optimization (Wiley, 2017).


    Google Scholar
     

  • Jiang, L., Lin, B. & Cai, Y. A model for predicting carbonation of high-volume fly ash concrete. Cem. Concr. Res. 30(5), 699–702 (2000).

    CAS 

    Google Scholar
     

  • Chang, C.-F. & Chen, J.-W. The experimental investigation of concrete carbonation depth. Cem. Concr. Res. 36(9), 1760–1767 (2006).

    ADS 
    CAS 

    Google Scholar
     

  • Balayssac, J., Détriché, C. H. & Grandet, J. Effects of curing upon carbonation of concrete. Constr. Build. Mater. 9(2), 91–95 (1995).


    Google Scholar
     

  • Roziere, E., Loukili, A. & Cussigh, F. A performance based approach for durability of concrete exposed to carbonation. Constr. Build. Mater. 23(1), 190–199 (2009).


    Google Scholar
     

  • Hussain, S., Bhunia, D. & Singh, S. Comparative study of accelerated carbonation of plain cement and fly-ash concrete. J. Build. Eng. 10, 26–31 (2017).


    Google Scholar
     

  • Younsi, A., Turcry, P., Aït-Mokhtar, A. & Staquet, S. Accelerated carbonation of concrete with high content of mineral additions: Effect of interactions between hydration and drying. Cem. Concr. Res. 43, 25–33 (2013).

    CAS 

    Google Scholar
     

  • Turcry, P., Oksri-Nelfia, L., Younsi, A. & Aït-Mokhtar, A. Analysis of an accelerated carbonation test with severe preconditioning. Cem. Concr. Res. 57, 70–78 (2014).

    CAS 

    Google Scholar
     

  • Chen, Y., Liu, P. & Yu, Z. Effects of environmental factors on concrete carbonation depth and compressive strength. Materials 11(11), 2167 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Golafshani, E. M., Behnood, A., Hosseinikebria, S. S. & Arashpour, M. Novel metaheuristic-based type-2 fuzzy inference system for predicting the compressive strength of recycled aggregate concrete. J. Clean. Prod. 320, 128771 (2021).


    Google Scholar
     

  • Moosavi, S. K. R. et al. A novel artificial neural network (ANN) using the mayfly algorithm for classification. In 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) (IEEE, 2021).

  • Hagan, M. T. & Menhaj, M. B. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994).

    CAS 
    PubMed 

    Google Scholar
     

  • Kazemi, R., Golafshani, E. M. & Behnood, A. Compressive strength prediction of sustainable concrete containing waste foundry sand using metaheuristic optimization-based hybrid artificial neural network. Struct. Concr. https://doi.org/10.1002/suco.202300313 (2023).

    Article 

    Google Scholar
     

  • Mehlig, B. Machine Learning with Neural Networks: An Introduction for Scientists and Engineers (Cambridge University Press, 2021).


    Google Scholar
     

  • Eskandari-Naddaf, H. & Kazemi, R. ANN prediction of cement mortar compressive strength, influence of cement strength class. Constr. Build. Mater. 138, 1–11 (2017).


    Google Scholar
     

  • Golafshani, E. M., Arashpour, M. & Kashani, A. Green mix design of rubbercrete using machine learning-based ensemble model and constrained multi-objective optimization. J. Clean. Prod. 327, 129518 (2021).


    Google Scholar
     

  • Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI, Montreal (1995).

  • Taylor, K. E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos. 106(D7), 7183–7192 (2001).

    ADS 

    Google Scholar
     



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