• Ahmad, J., Zhou, Z., Majdi, A., Alqurashi, M. & Deifalla, A. F. Overview of concrete performance made with waste rubber tires: A step toward sustainable concrete. Materials (Basel) 15, 5518 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Meesala, C. R. Influence of different types of fiber on the properties of recycled aggregate concrete. Struct. Concr. 20, 1656–1669 (2019).

    Article 

    Google Scholar
     

  • Su, Y. et al. Modification of recycled concrete aggregate and its use in concrete: An overview of research progress. Materials (Basel) 16, 7144 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Ahmad, J. et al. A review on sustainable concrete with the partially substitutions of silica fume as a cementitious material. Sustainability 14, 12075 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Manan, A. et al. Physical properties of recycled concrete powder and waste tyre fibre reinforced concrete. Proc. Inst. Civ. Eng. Eng. Sustain. 1–14 (2024) https://doi.org/10.1680/jensu.24.00079.

  • Idir, R., Cyr, M. & Tagnit-Hamou, A. Use of fine glass as ASR inhibitor in glass aggregate mortars. Constr. Build. Mater. 24, 1309–1312 (2010).

    Article 

    Google Scholar
     

  • Al-Zubaidi, A. B. & Al-Tabbakh, A. A. Recycling glass powder and its use as cement mortar applications. Int. J. Sci. Eng. Res. 7, 555–564 (2016).


    Google Scholar
     

  • Li, H., Xu, Y., Chen, P., Ge, J. & Wu, F. Impact energy consumption of high-volume rubber concrete with silica fume. Adv. Civ. Eng. 2019, 1–11 (2019).


    Google Scholar
     

  • Guerra, I., Vivar, I., Llamas, B., Juan, A. & Moran, J. Eco-efficient concretes: The effects of using recycled ceramic material from sanitary installations on the mechanical properties of concrete. Waste Manag. 29, 643–646 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Malešev, M., Radonjanin, V. & Marinković, S. Recycled concrete as aggregate for structural concrete production. Sustainability 2, 1204–1225 (2010).

    Article 

    Google Scholar
     

  • Butler, L., West, J. S. & Tighe, S. L. Effect of recycled concrete coarse aggregate from multiple sources on the hardened properties of concrete with equivalent compressive strength. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2013.05.074 (2013).

    Article 

    Google Scholar
     

  • Zhu, L., Ning, Q., Han, W. & Bai, L. Compressive strength and microstructural analysis of recycled coarse aggregate concrete treated with silica fume. Constr. Build. Mater. 334, 127453 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Ismail, S. & Ramli, M. Engineering properties of treated recycled concrete aggregate (RCA) for structural applications. Constr. Build. Mater. 44, 464–476 (2013).

    Article 

    Google Scholar
     

  • Turhollow, A. et al. The updated billion-ton resource assessment. Biomass Bioenerg. https://doi.org/10.1016/j.biombioe.2014.09.007 (2014).

    Article 

    Google Scholar
     

  • Turk, J., Cotič, Z., Mladenovič, A. & Šajna, A. Environmental evaluation of green concretes versus conventional concrete by means of LCA. Waste Manag. 45, 194–205 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Kang, T. Performance of concrete structures with unique materials, reinforcement or geometry. Int. J. Concrete Struct. Mater. 7, 1–2 (2013).

    Article 

    Google Scholar
     

  • Makul, N. et al. Design strategy for recycled aggregate concrete: A review of status and future perspectives. Crystals 11, 695 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Makul, N. et al. Use of recycled concrete aggregates in production of green cement-based concrete composites: A review. Crystals 11, 232 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Wijayasundara, M., Mendis, P. & Crawford, R. H. Methodology for the integrated assessment on the use of recycled concrete aggregate replacing natural aggregate in structural concrete. J. Clean. Prod. 166, 321–334 (2017).

    Article 

    Google Scholar
     

  • Tabsh, S. W. & Abdelfatah, A. S. Influence of recycled concrete aggregates on strength properties of concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2008.06.007 (2009).

    Article 

    Google Scholar
     

  • Manan, A., Pu, Z. & Sabri, M. M. Environmental and human health impact of recycle concrete powder: An emergy-based LCA approach. Front. Environ. Sci. 12, 1505312. https://doi.org/10.3389/fenvs.2024.1505312 (2025).

    Article 

    Google Scholar
     

  • Han, S., Zhao, S., Lu, D. & Wang, D. Performance improvement of recycled concrete aggregates and their potential applications in infrastructure: A review. Buildings 13, 1411 (2023).

    Article 

    Google Scholar
     

  • Manan, A. et al. AI-based constitutive model simulator for predicting the axial load-deflection behavior of recycled concrete powder and steel fiber reinforced concrete column. Constr. Build. Mater. 470, 140628 (2025).

    Article 
    CAS 

    Google Scholar
     

  • Chu, H. H. et al. Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete. Ain Shams Eng. J. https://doi.org/10.1016/j.asej.2021.03.018 (2021).

    Article 

    Google Scholar
     

  • Manan, A., Zhang, P., Ahmad, S. & Ahmad, J. Prediction of flexural strength in FRP bar reinforced concrete beams through a machine learning approach. Anti-Corrosion Methods Mater. 71, 562–579 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Tipu, R. K., Batra, V., Suman, Pandya, K. S. & Panchal, V. R. Enhancing load capacity prediction of column using eReLU-activated BPNN model. Structures (2023) https://doi.org/10.1016/j.istruc.2023.105600.

  • Tipu, R. K., Batra, V., Suman, Pandya, K. S. & Panchal, V. R. Efficient compressive strength prediction of concrete incorporating recycled coarse aggregate using Newton’s boosted backpropagation neural network (NB-BPNN). Structures (2023) https://doi.org/10.1016/j.istruc.2023.105559.

  • Mohamed, H. S. et al. Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques. Sci. Rep. 14, 27007 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tipu, R. K. et al. Optimizing compressive strength in sustainable concrete: A machine learning approach with iron waste integration. Asian J. Civ. Eng. 25, 4487–4512 (2024).

    Article 

    Google Scholar
     

  • Parhi, S. K. & Patro, S. K. Prediction of compressive strength of geopolymer concrete using a hybrid ensemble of grey wolf optimized machine learning estimators. J. Build. Eng. https://doi.org/10.1016/j.jobe.2023.106521 (2023).

    Article 

    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. https://doi.org/10.1007/s42107-023-00799-8 (2024).

    Article 

    Google Scholar
     

  • Kumar Dash, P., Kumar Parhi, S., Kumar Patro, S. & Panigrahi, R. Efficient machine learning algorithm with enhanced cat swarm optimization for prediction of compressive strength of GGBS-based geopolymer concrete at elevated temperature. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2023.132814 (2023).

    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. Des. https://doi.org/10.1007/s41939-023-00349-4 (2024).

    Article 

    Google Scholar
     

  • Parhi, S. K. & Patro, S. K. Parametric analysis and prediction of geopolymerization process. Mater. Today Commun. 41, 111047 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Dash, P. K., Parhi, S. K., Patro, S. K. & Panigrahi, R. Influence of chemical constituents of binder and activator in predicting compressive strength of fly ash-based geopolymer concrete using firefly-optimized hybrid ensemble machine learning model. Mater. Today Commun. https://doi.org/10.1016/j.mtcomm.2023.107485 (2023).

    Article 

    Google Scholar
     

  • Parhi, S. K., Nanda, A. & Panigrahi, S. K. Multi-objective optimization and prediction of strength along with durability in acid-resistant self-compacting alkali-activated concrete. Constr. Build. Mater. 456, 139235 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Singh, S., Patro, S. K. & Parhi, S. K. Evolutionary optimization of machine learning algorithm hyperparameters for strength prediction of high-performance concrete. Asian J. Civ. Eng. https://doi.org/10.1007/s42107-023-00698-y (2023).

    Article 

    Google Scholar
     

  • Bui, D.-K., Nguyen, T., Chou, J.-S., Nguyen-Xuan, H. & Ngo, T. D. A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Constr. Build. Mater. 180, 320–333 (2018).

    Article 

    Google Scholar
     

  • Mansour, M. Y., Dicleli, M., Lee, J.-Y. & Zhang, J. Predicting the shear strength of reinforced concrete beams using artificial neural networks. Eng. Struct. 26, 781–799 (2004).

    Article 

    Google Scholar
     

  • Shahmansouri, A. A., Bengar, H. A. & Ghanbari, S. Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method. J. Build. Eng. 31, 101326 (2020).

    Article 

    Google Scholar
     

  • Behnood, A., Olek, J. & Glinicki, M. A. Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm. Constr. Build. Mater. 94, 137–147 (2015).

    Article 

    Google Scholar
     

  • Kim, J. & Jang, H. Closed-loop recycling of C&D waste: Mechanical properties of concrete with the repeatedly recycled C&D powder as partial cement replacement. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2022.130977 (2022).

    Article 

    Google Scholar
     

  • Bogas, J. A., Carriço, A. & Pereira, M. F. C. Mechanical characterization of thermal activated low-carbon recycled cement mortars. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2019.01.325 (2019).

    Article 

    Google Scholar
     

  • Zhao, S. Y., Li, Y., Kang, X. M. & Fan, Y. H. Experimental study on frost resistance of recycled fine powder concrete. Ind. Constr 50, 112–118 (2020).


    Google Scholar
     

  • Gao, S. Full-component of waste cement and utilization of recycled concrete (2019).

  • Duan, Z., Singh, A., Xiao, J. & Hou, S. Combined use of recycled powder and recycled coarse aggregate derived from construction and demolition waste in self-compacting concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2020.119323 (2020).

    Article 

    Google Scholar
     

  • Kim, Y. J. Quality properties of self-consolidating concrete mixed with waste concrete powder. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2016.12.174 (2017).

    Article 

    Google Scholar
     

  • Zhang, H., Xiao, J., Tang, Y., Duan, Z. & Poon, C. S. Long-term shrinkage and mechanical properties of fully recycled aggregate concrete: Testing and modelling. Cem. Concr. Compos. 130, 104527. https://doi.org/10.1016/j.cemconcomp.2022.104527 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Wu, H., Yang, D., Xu, J., Liang, C. & Ma, Z. Water transport and resistance improvement for the cementitious composites with eco-friendly powder from various concrete wastes. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2021.123247 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Wu, R. et al. Tensile behavior of strain hardening cementitious composites (Shcc) containing reactive recycled powder from various c&d waste. J. Renew. Mater. https://doi.org/10.32604/jrm.2021.013669 (2021).

    Article 

    Google Scholar
     

  • Wu, H., Liang, C., Xiao, J., Xu, J. & Ma, Z. Early-age behavior and mechanical properties of cement-based materials with various types and fineness of recycled powder. Struct. Concr. https://doi.org/10.1002/suco.202000834 (2022).

    Article 

    Google Scholar
     

  • Li, S., Gao, J., Li, Q. & Zhao, X. Investigation of using recycled powder from the preparation of recycled aggregate as a supplementary cementitious material. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2020.120976 (2021).

    Article 

    Google Scholar
     

  • Ma, Z., Shen, J., Wu, H. & Zhang, P. Properties and activation modification of eco-friendly cementitious materials incorporating high-volume hydrated cement powder from construction waste. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2021.125788 (2022).

    Article 

    Google Scholar
     

  • Xiao, J., Ma, Z., Sui, T., Akbarnezhad, A. & Duan, Z. Mechanical properties of concrete mixed with recycled powder produced from construction and demolition waste. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2018.03.277 (2018).

    Article 

    Google Scholar
     

  • He, X. et al. Humid hardened concrete waste treated by multiple wet-grinding and its reuse in concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2022.128485 (2022).

    Article 

    Google Scholar
     

  • Ma, Z., Yao, P., Yang, D. & Shen, J. Effects of fire-damaged concrete waste on the properties of its preparing recycled aggregate, recycled powder and newmade concrete. J. Mater. Res. Technol. https://doi.org/10.1016/j.jmrt.2021.08.116 (2021).

    Article 

    Google Scholar
     

  • Letelier, V., Tarela, E., Muñoz, P. & Moriconi, G. Combined effects of recycled hydrated cement and recycled aggregates on the mechanical properties of concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2016.12.010 (2017).

    Article 

    Google Scholar
     

  • Cantero, B., Bravo, M., de Brito, J., del Bosque, I. F. S. & Medina, C. Thermal performance of concrete with recycled concrete powder as partial cement replacement and recycled CDW aggregate. Appl. Sci. https://doi.org/10.3390/app10134540 (2020).

    Article 

    Google Scholar
     

  • Sun, C., Chen, Q., Xiao, J. & Liu, W. Utilization of waste concrete recycling materials in self-compacting concrete. Resour. Conserv. Recycl. 161, 104930 (2020).

    Article 

    Google Scholar
     

  • Tang, Y., Xiao, J., Zhang, H., Duan, Z. & Xia, B. Mechanical properties and uniaxial compressive stress-strain behavior of fully recycled aggregate concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2022.126546 (2022).

    Article 

    Google Scholar
     

  • Quan, H. & Kasami, H. Experimental study on the effects of recycled concrete powder on properties of self-compacting concrete. Open Civ. Eng. J. https://doi.org/10.2174/1874149501812010430 (2018).

    Article 

    Google Scholar
     

  • Lv, F. et al. An improved extreme gradient boosting approach to vehicle speed prediction for construction simulation of earthwork. Autom. Constr. https://doi.org/10.1016/j.autcon.2020.103351 (2020).

    Article 

    Google Scholar
     

  • Yoo, K., Shukla, S. K., Ahn, J. J., Oh, K. & Park, J. Decision tree-based data mining and rule induction for identifying hydrogeological parameters that influence groundwater pollution sensitivity. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2016.01.075 (2016).

    Article 

    Google Scholar
     

  • Taunk, K., De, S., Verma, S. & Swetapadma, A. A brief review of nearest neighbor algorithm for learning and classification. In 2019 international conference on intelligent computing and control systems, ICCS 2019 (2019). https://doi.org/10.1109/ICCS45141.2019.9065747.

  • Manan, A., Zhang, P., Ahmad, S., Umar, M. & Raza, A. Machine learning prediction model integrating experimental study for compressive strength of carbon-nanotubes composites. J. Eng. Res. https://doi.org/10.1016/j.jer.2024.08.007 (2024).

    Article 

    Google Scholar
     

  • Chicco, D., Warrens, M. J. & Jurman, G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci. https://doi.org/10.7717/PEERJ-CS.623 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kadhim, Z. S., Abdullah, H. S. & Ghathwan, K. I. Artificial neural network hyperparameters optimization: A survey. Int. J. Online Biomed. Eng. https://doi.org/10.3991/ijoe.v18i15.34399 (2022).

    Article 

    Google Scholar
     

  • Kumar, N., Maurya, V. & Kumar Maurya, V. a Review on machine learning (feature selection, classification and clustering) approaches of big data mining in different area of research journal of critical reviews a review on machine learning (feature selection, classification and clustering) approac. Artic. J. Crit. Rev. 7, 2020 (2020).


    Google Scholar
     

  • Fushiki, T. Estimation of prediction error by using K-fold cross-validation. Stat. Comput. https://doi.org/10.1007/s11222-009-9153-8 (2011).

    Article 
    MathSciNet 

    Google Scholar
     

  • Manan, A., Zhang, P., Ahmad, S. & Ahmad, J. Optimizing hybrid fibre-reinforced polymer bars design: A machine learning approach.

  • Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, (2017).

  • Manan, A. et al. Machine learning prediction of recycled concrete powder with experimental validation and life cycle assessment study. Case Stud. Constr. Mater. 21, e04053 (2024).


    Google Scholar
     

  • Abdulalim Alabdullah, A. et al. Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2022.128296 (2022).

    Article 

    Google Scholar
     



  • Source link