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Estimation of compressive strength of recycled aggregate concrete using advanced meta-heuristic algorithms and random forest analysis

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Abstract

This study uses random forest (RF) analysis to present a novel method for estimating the compressive strength (Fc) of concrete made with recycled aggregate (RAC). The intricate interactions between various components make it challenging to achieve the desired Fc through the best mixture proportions. The 441 samples are used with six inputs, including Coarse Aggregate to Cement Ratio, Cement Fineness, Fine Aggregate to Total Aggregate Ratio, Specific Gravity of Saturated Surface-Dry Aggregates, and Water Absorption of Aggregates. Algorithms for machine learning (ML) and artificial intelligence (AI) have shown significant promise for solving this complexity. In addition, a semi-empirical strategy is offered that seamlessly incorporates optimization methods to improve prediction precision. The accuracy of models is improved using the Improved Grey Wolf Optimizer (IGWO) and Flying Foxes Optimization (FFO). Therefore, three distinct models are produced: RFFO, RFIG, and a standalone RF model. Each of these models provides essential insights into accurate Fc prediction for RAC. Interestingly, the RFFO model outperforms all others with a remarkable R2 value of 0.996 and a remarkably low RMSE value of 0.993. Furthermore, RF obtained a weak performance equal to 4.897 compared to other developed models, in which the RFFO achieved 0.986. These results demonstrate the RFFO model's skill in predicting RAC results while reiterating its accuracy and dependability. This technique has great potential for precise RAC prediction in the construction sector.

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References

  • Abdollahzadeh G, Jahani E, Kashir Z (2016) Predicting of compressive strength of recycled aggregate concrete by genetic programming. Comput Concrete 18(2):155–163

    Article  Google Scholar 

  • Ajdukiewicz A, Kliszczewicz A (2002) Influence of recycled aggregates on mechanical properties of HS/HPC. Cem Concr Compos 24(2):269–279

    Article  Google Scholar 

  • Akbarzadeh MR, Ghafourian H, Anvari A, Pourhanasa R, Nehdi ML (2023) Estimating compressive strength of concrete using neural electromagnetic field optimization. Materials 16(11):4200

    Article  Google Scholar 

  • Behnood A, Olek J, Glinicki MA (2015) Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm. Constr Build Mater 94:137–147. https://doi.org/10.1016/j.conbuildmat.2015.06.055

    Article  Google Scholar 

  • Biau G, Scornet E (2016) A random forest guided tour. TEST 25:197–227

    Article  MathSciNet  Google Scholar 

  • Casuccio M, Torrijos MC, Giaccio G, Zerbino R (2008) Failure mechanism of recycled aggregate concrete. Constr Build Mater 22(7):1500–1506. https://doi.org/10.1016/j.conbuildmat.2007.03.032

    Article  Google Scholar 

  • Dilbas H, Şimşek M, Çakır Ö (2014) An investigation on mechanical and physical properties of recycled aggregate concrete (RAC) with and without silica fume. Constr Build Mater 61:50–59. https://doi.org/10.1016/j.conbuildmat.2014.02.057

    Article  Google Scholar 

  • Duan ZH, Poon CS (2014) Properties of recycled aggregate concrete made with recycled aggregates with different amounts of old adhered mortars. Mater Des 58:19–29. https://doi.org/10.1016/j.matdes.2014.01.044

    Article  Google Scholar 

  • Duan Z-H, Kou S-C, Poon CS (2013a) Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Constr Build Mater 40:1200–1206. https://doi.org/10.1016/j.conbuildmat.2012.04.063

    Article  Google Scholar 

  • Duan Z-H, Kou S-C, Poon CS (2013b) Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete. Constr Build Mater 44:524–532. https://doi.org/10.1016/j.conbuildmat.2013.02.064

    Article  Google Scholar 

  • Duan J, Asteris PG, Nguyen H, Bui X-N, Moayedi H (2021) A novel artificial intelligence technique to predict the compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput 37:3329–3346

    Article  Google Scholar 

  • Folino P, Xargay H (2014) Recycled aggregate concrete–mechanical behaviour under uniaxial and triaxial compression. Constr Build Mater 56:21–31. https://doi.org/10.1016/j.conbuildmat.2014.01.073

    Article  Google Scholar 

  • Gao Z-M, Zhao J (2019) An improved grey wolf optimization algorithm with variable weights. Comput Intell Neurosci 2019:1–13

    Article  Google Scholar 

  • Gholampour A, Gandomi AH, Ozbakkaloglu T (2017) New formulations for mechanical properties of recycled aggregate concrete using gene expression programming. Constr Build Mater 130:122–145. https://doi.org/10.1016/j.conbuildmat.2016.10.114

    Article  Google Scholar 

  • Golafshani EM, Behnood A (2018) Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete. J Clean Prod 176:1163–1176

    Article  Google Scholar 

  • Golafshani EM, Behnood A (2018) Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete. Appl Soft Comput 64:377–400. https://doi.org/10.1016/j.asoc.2017.12.030

    Article  Google Scholar 

  • Gonzalez-Fonteboa B, Martinez-Abella F, Eiras-Lopez J, Seara-Paz S (2011) Effect of recycled coarse aggregate on the damage of recycled concrete. Mater Struct 44(10):1759–1771. https://doi.org/10.1617/s11527-011-9736-7

    Article  Google Scholar 

  • Han T, Siddique A, Khayat K, Huang J, Kumar A (2020) An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete. Constr Build Mater 244:118271

    Article  Google Scholar 

  • Haupt RL, Haupt SE (2004) Practical genetic algorithms. John Wiley & Sons

  • Haykin S (2009) Neural networks and learning machines, 3rd edn. China Machine Press

  • Hoang N-D (2023) “A novel ant colony-optimized extreme gradient boosting machine for estimating the compressive strength of recycled aggregate concrete.” Multiscale Multidiscip Model Exp Des. https://doi.org/10.1007/s41939-023-00220-6

    Article  Google Scholar 

  • Isgor OB, Ideker J, Fu T, Adams MP (2013) Cracking susceptibility of concrete made with recycled concrete aggregate. OTREC-SS-725. Transportation Research and Education Center (TREC), Portland, OR. https://doi.org/10.15760/trec.50

  • Khan K, Ahmad W, Amin MN, Aslam F, Ahmad A, Al-Faiad MA (2022) Comparison of prediction models based on machine learning for the compressive strength estimation of recycled aggregate concrete. Materials 15(10):3430. https://doi.org/10.3390/ma15103430

    Article  Google Scholar 

  • Kou S, Poon C, Agrela F (2011) Comparisons of natural and recycled aggregate concrete prepared with the addition of different mineral admixtures. Cem Concr Compos 33(8):788–795

    Article  Google Scholar 

  • Kulkarni AD, Lowe B (2016) “Random forest algorithm for land cover classification,”

  • Kumar S, Tejani GG, Mirjalili S (2019) Modified symbiotic organisms search for structural optimization. Eng Comput 35(4):1269–1296

    Article  Google Scholar 

  • Kumar S, Tejani GG, Pholdee N, Bureerat S (2021) Multi-objective passing vehicle search algorithm for structure optimization. Expert Syst Appl 169:114511

    Article  Google Scholar 

  • Lin W, Wu Z, Lin L, Wen A, Li J (2017) An ensemble random forest algorithm for insurance big data analysis. IEEE Access 5:16568–16575

    Article  Google Scholar 

  • Liu Z, Jiang P, Wang J, Zhang L (2021) Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm. Expert Syst Appl 177:114974

    Article  Google Scholar 

  • Lovato PS, Possan E, Dal Molin DCC, Masuero ÂB, Ribeiro JLD (2012) Modeling of mechanical properties and durability of recycled aggregate concretes. Constr Build Mater 26(1):437–447

    Article  Google Scholar 

  • Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917

    Article  Google Scholar 

  • Nobile MS, Cazzaniga P, Besozzi D, Colombo R, Mauri G, Pasi G (2018) Fuzzy self-tuning PSO: a settings-free algorithm for global optimization. Swarm Evol Comput 39:70–85

    Article  Google Scholar 

  • Onyelowe KC, Gnananandarao T, Ebid AM, Mahdi HA, Ghadikolaee MR, Al-Ajamee M (2022) Evaluating the compressive strength of recycled aggregate concrete using novel artificial neural network. Civil Eng J 8(8):1679–1693

    Article  Google Scholar 

  • Rahal K (2007) Mechanical properties of concrete with recycled coarse aggregate. Build Environ 42(1):407–415. https://doi.org/10.1016/j.buildenv.2005.07.033

    Article  Google Scholar 

  • Safarian S, Ebrahimi Saryazdi SM, Unnthorsson R, Richter C (2021) Modeling of hydrogen production by applying biomass gasification: artificial neural network modelling approach. Fermentation 7(2):71

    Article  Google Scholar 

  • Sarica A, Cerasa A, Quattrone A (2017) Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front Aging Neurosci 9:329

    Article  Google Scholar 

  • Schoppe BM (2011) Shrinkage & modulus of elasticity in concrete with recycled aggregates. Master’s Theses. https://doi.org/10.15368/theses.2011.62

  • Shi C, Li Y, Zhang J, Li W, Chong L, Xie Z (2016) Performance enhancement of recycled concrete aggregate–a review. J Clean Prod 112:466–472

    Article  Google Scholar 

  • Sullivan E (2022) Understanding from machine learning models. Br J Philos Sci. https://doi.org/10.1093/bjps/axz035

    Article  MathSciNet  Google Scholar 

  • Tavana Amlashi A, Mohammadi Golafshani E, Ebrahimi SA, Behnood A (2023) Estimation of the compressive strength of green concrete containing rice husk ash: a comparison of different machine learning approaches. Eur J Environ Civ Eng 27(2):961–983. https://doi.org/10.1080/19648189.2022.2068657

    Article  Google Scholar 

  • Thomas C, Setién J, Polanco J, Alaejos P, De Juan MS (2013) “Durability of recycled aggregate concrete.” Constr Build Mater 40:1054–1065

    Article  Google Scholar 

  • Tsafarakis S, Zervoudakis K, Andronikidis A, Altsitsiadis E (2020) Fuzzy self-tuning differential evolution for optimal product line design. Eur J Oper Res 287(3):1161–1169

    Article  MathSciNet  Google Scholar 

  • Wang H, Lei Z, Zhang X, Zhou B, Peng J (2016) “Machine learning basics,” Deep Learn, 98–164

  • Wardeh G, Ghorbel E, Gomart H (2015) Mix design and properties of recycled aggregate concretes applicability of Eurocode 2. Int J Concr Struct Mater 9:1–20

    Article  Google Scholar 

  • Xu J, Zhao X, Yu Y, Xie T, Yang G, Xue J (2019) Parametric sensitivity analysis and modelling of mechanical properties of normal-and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks. Constr Build Mater 211:479–491

    Article  Google Scholar 

  • Zega CJ, Di Maio AA (2009) Recycled concrete made with different natural coarse aggregates exposed to high temperature. Constr Build Mater 23(5):2047–2052. https://doi.org/10.1016/j.conbuildmat.2008.08.017

    Article  Google Scholar 

  • Zervoudakis K, Tsafarakis S (2022) A global optimizer inspired by the survival strategies of flying foxes. Eng Comput. https://doi.org/10.1007/s00366-021-01554-w

    Article  Google Scholar 

  • Zhou ZH (2021) Machine learning. Springer Nature, Singapore

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported by the Science and Technology Research Program of the Chongqing Municipal Education Commission (Grant NO. KJQN202105702).

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  1. College of Road, Bridge and Architecture, Chongqing Vocational College of Transportation, Jiangjin, Chongqing, 402260, China

    Qiuyun Wang

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  1. Qiuyun Wang
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The author contributed to the study's conception and design. Data collection, simulation and analysis were performed by “Qiuyun Wang”.

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Wang, Q. Estimation of compressive strength of recycled aggregate concrete using advanced meta-heuristic algorithms and random forest analysis. Multiscale and Multidiscip. Model. Exp. and Des. (2024). https://doi.org/10.1007/s41939-024-00413-7

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