Predictive modelling on the compressive strength and fresh properties of self-compacting recycled aggregate concrete using machine learning approaches
DOI:
https://doi.org/10.3989/mc.2025.382624Keywords:
Self compacting recycled aggregate concrete, Multi layer perceptron, Gradient boost regression, Bagging regression, Extreme gradient boost regression, K nearest neighbours regression, Support vector regression, K fold cross validation, Multi objective pareto optimisationAbstract
Self-Compacting Recycled Aggregate Concrete (SCRAC) has emerged as a practical engineering solution to recycle the construction aggregates. This study focusses on developing and comparing six popular Machine Learning (ML) models based on Multi Layer Perceptron regression (MLP), Gradient Boost Regression (GBR), Bagging regression (BG), Extreme Gradient Boost regression (XGB), K Nearest Neighbours regression (KNN), and Support Vector Regression (SVR), along with six metamodels prepared by stacking of these ML models, for prediction of each, Compressive Strength (CS), Slump Flow (SF) and V Funnel time (VF) of the SCRAC. Eight mix ratios were used as the input features. The best performance ranking was exhibited by Gradient Boost model GBR_CS for CS prediction whereas stacked models performed best for prediction of SF and VF. Additionally, multi-objective optimization and partial Pareto fronts were constructed to analyse trade-offs between CS, SF, cost, and CO2 emission.
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Aslam MS, Huang B, Cui L. 2020. Review of construction and demolition waste management in China and USA. J. Environ. Manage. 264:110445. https://doi.org/10.1016/j.jenvman.2020.110445 PMid:32217323
Moschen-Schimek J, Kasper T, Huber-Humer M. 2023. Critical review of the recovery rates of construction and demolition waste in the European Union - An analysis of influencing factors in selected EU countries. Waste Manage. 167:150-164. https://doi.org/10.1016/j.wasman.2023.05.020 PMid:37267878
Jain MS. 2021. A mini review on generation, handling, and initiatives to tackle construction and demolition waste in India. Environ. Technol. Innov. 22:101490 https://doi.org/10.1016/j.eti.2021.101490
Huang P, Dai K, Yu X. 2023. Machine learning approach for investigating compressive strength of self-compacting concrete containing supplementary cementitious materials and recycled aggregate. J. Build. Eng. 79:107904. https://doi.org/10.1016/j.jobe.2023.107904
Wang Z, Wu B. 2023. A mix design method for self-compacting recycled aggregate concrete targeting slump-flow and compressive strength. Constr. Build. Mater. 404:133309. https://doi.org/10.1016/j.conbuildmat.2023.133309
Abed M, Nemes R, Tayeh BA. 2020. Properties of self-compacting high-strength concrete containing multiple use of recycled aggregate. Journal of King Saud University - Engineering Sciences. 32(2):108-114. https://doi.org/10.1016/j.jksues.2018.12.002
Martínez-García R, Guerra-Romero IM, Morán-del Pozo JM, de Brito J, Juan-Valdés A. 2020. Recycling aggregates for self-compacting concrete production: A feasible option. Materials. 13(4):868. https://doi.org/10.3390/ma13040868 PMid:32075141 PMCid:PMC7078595
Abed M, Rashid K, Rehman MU, Ju M. 2022. Performance keys on self-compacting concrete using recycled aggregate with fly ash by multi-criteria analysis. J. Clean. Prod. 378:134398. https://doi.org/10.1016/j.jclepro.2022.134398
Wang Z, Wu B. 2023. A mix design method for self-compacting recycled aggregate concrete targeting slump-flow and compressive strength. Constr. Build. Mater. 404:133309. https://doi.org/10.1016/j.conbuildmat.2023.133309
Chen X, Lv L, Wu Q, Cheng S, Zhou Q, Zhao C, Fan T, Zhao R. 2023. Optimization on the mix design method of self-compacting concrete with recycled coarse aggregate based on paste rheological threshold theory and material packing characteristics. Constr. Build. Mater. 407:133509. https://doi.org/10.1016/j.conbuildmat.2023.133509
Long W, Cheng B, Luo S, Li L, Mei L. 2023. Interpretable auto-tune machine learning prediction of strength and flow properties for self-compacting concrete. Constr. Build. Mater. 393:132101. https://doi.org/10.1016/j.conbuildmat.2023.132101
Mahmood MS, Elahi A, Zaid O, Alashker Y, Șerbănoiu AA, Grădinaru CM, Ullah K, Ali T. 2023. Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder. Case Stud. Constr. Mater. 19:e02557. https://doi.org/10.1016/j.cscm.2023.e02557
Alarfaj M, Qureshi HJ, Shahab MZ, Javed MF, Arifuzzaman M, Gamil Y. 2024. Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete. Case Stud. Constr. Mater. 20:e02836. https://doi.org/10.1016/j.cscm.2023.e02836
Alyaseen A, Poddar A, Kumar N, Tajjour S, Prasad CVSR, Alahmad H, Sihag P. 2023. High-performance self-compacting concrete with recycled coarse aggregate: Soft-computing analysis of compressive strength. J. Build. Eng. 77:107527. https://doi.org/10.1016/j.jobe.2023.107527
Alyaseen A, Poddar A, Kumar N, Sihag P, Lee D, kumar R, Singh T. 2023. Assessing the compressive and splitting tensile strength of self-compacting recycled coarse aggregate concrete using machine learning and statistical techniques. Mater. Today Commun. 38:107970. https://doi.org/10.1016/j.mtcomm.2023.107970
de-Prado-Gil J, Martínez-García R, Jagadesh P, Juan-Valdés A, Gónzalez-Alonso M-I, Palencia C. 2023. To determine the compressive strength of self-compacting recycled aggregate concrete using artificial neural network ANN. Ain Shams Eng. J. 15(2):102548. https://doi.org/10.1016/j.asej.2023.102548
De-Prado-gil J, Zaid O, Palencia C, Martínez-García R. 2022. Prediction of splitting tensile strength of self-compacting recycled aggregate concrete using novel deep learning methods. Mathematics 10(13):2245. https://doi.org/10.3390/math10132245
Huang P, Dai K, Yu X. 2023. Machine learning approach for investigating compressive strength of self-compacting concrete containing supplementary cementitious materials and recycled aggregate. J. Build. Eng. 79:107904. https://doi.org/10.1016/j.jobe.2023.107904
Jagadesh P, de Prado-Gil J, Silva-Monteiro N, Martínez-García R. 2023. Assessing the compressive strength of self-compacting concrete with recycled aggregates from mix ratio using machine learning approach. J. Mater. Res. Technol. 24:1483-1498. https://doi.org/10.1016/j.jmrt.2023.03.037
Ghahdarijani AM, Hormozi F, Asl AH. 2017. Convective heat transfer and pressure drop study on nanofluids in double-walled reactor by developing an optimal multilayer perceptron artificial neural network. Int. Commun. Heat Mass Transf. 84:11-19. https://doi.org/10.1016/j.icheatmasstransfer.2017.03.014
Yao J, Huang S, Xu Y, Gu C, Liu J, Yang Y, Ni T, Kong D. 2023. Mix design of equal strength high volume fly ash concrete with artificial neural network. Case Stud. Constr. Mater. 19:e02294. https://doi.org/10.1016/j.cscm.2023.e02294
Friedman JH. 2001. Reitz lecture greedy function approximation: a gradient boosting machine 1:1189-1232. https://doi.org/10.1214/aos/1013203451
Louk MHL, Tama BA. 2023. Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system. Expert Syst. Appl. 213(Part B):119030. https://doi.org/10.1016/j.eswa.2022.119030
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