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Comparison of artificial neural network and hierarchical regression in prediction compressive strength of self-compacting concrete with fly ash

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Abstract

This study aims to predict and model the compressive strength of self-compacting concrete (SCC) across various fly ash content ranges. The research utilized two approaches: hierarchical regression (HR) and artificial neural networks (ANN) for modeling six variables influencing the process (cement content, fly ash content, water-to-binder ratio (W/B), coarse aggregate, fine aggregate, and superplasticizer). The fly ash content varied from 0 to 60% of the total weight of cement. The findings emphasize that the compressive strength of SCC is significantly affected by all the independent variables studied, except for superplasticizer. The statistical evaluation using the Pearson correlation (R), determination coefficient (R2), Adjusted R2, Predicted R2, root mean square error (RMSE), mean square error (MSE) and mean absolute percentage error (MAPE) demonstrate that both ANN and HR are robust tools for predicting compressive strength of SCC. Additionally, the ANN and HR models show strong correlations with experimental data, with the ANN model displaying superior accuracy. As the performance indices showed, the ANN model had a higher predictive accuracy than HR. The ANN model had a higher determination coefficient (R2) of 98.51%, compared to 95.25% for HR, indicating a higher accuracy.

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References

  1. Zongjin L (2011) Advanced concrete technology. Wiley, Hoboken

    Google Scholar 

  2. EFNARC (2005) The European guidelines for self-compacting concrete,” Eur. Guidel Self Compact Concr. p. 63, [Online]. Available: http://www.efnarc.org/pdf/SCCGuidelinesMay2005.pdf

  3. Neville AM (2005) Properties of concrete, 4th edn. Pearson Education Limited

  4. Khatib JM (2008) Performance of self-compacting concrete containing fly ash. Constr Build Mater 22(9):1963–1971. https://doi.org/10.1016/j.conbuildmat.2007.07.011

    Article  Google Scholar 

  5. Liu M (2010) Self-compacting concrete with different levels of pulverized fuel ash. Constr Build Mater 24(7):1245–1252. https://doi.org/10.1016/j.conbuildmat.2009.12.012

    Article  Google Scholar 

  6. Barbhuiya S (2011) Effects of fly ash and dolomite powder on the properties of self-compacting concrete. Constr Build Mater 25(8):3301–3305. https://doi.org/10.1016/j.conbuildmat.2011.03.018

    Article  Google Scholar 

  7. Sanni SH, Khadiranaikar RB, Ash F (1989) Performance of self compacting concrete incorporating fly ash. J Mech Civil Eng 15:33–37

    Google Scholar 

  8. Sadrossadat E, Basarir H, Karrech A, Elchalakani M (2022) An engineered ML model for prediction of the compressive strength of eco-SCC based on type and proportions of materials. Clean Mater 4:100072. https://doi.org/10.1016/j.clema.2022.100072

    Article  CAS  Google Scholar 

  9. Siddique R, Aggarwal P, Aggarwal Y, Gupta SM (2008) Modeling properties of self-compacting concrete: support vector machines approach. Comput Concr 5(5):461–473. https://doi.org/10.12989/cac.2008.5.5.461

    Article  Google Scholar 

  10. Boukhatem B, Kenai S, Tagnit-Hamou A, Ghrici M (2011) Application of new information technology on concrete: an overview. J Civ Eng Manag 17(2):248–258. https://doi.org/10.3846/13923730.2011.574343

    Article  Google Scholar 

  11. Alzabeebee S, Al-Hamd RKS, Nassr A, Kareem M, Keawsawasvong S (2023) Multiscale soft computing-based model of shear strength of steel fibre-reinforced concrete beams. Innov Infrastruct Solut 8:1. https://doi.org/10.1007/s41062-022-01028-y

    Article  Google Scholar 

  12. Asteris PG, Kolovos KG, Douvika MG, Roinos K (2016) Prediction of self-compacting concrete strength using artificial neural networks. Eur J Environ Civ Eng 20:s102–s122. https://doi.org/10.1080/19648189.2016.1246693

    Article  Google Scholar 

  13. Da Silva WRL, Štemberk P (2013) Expert system applied for classifying self-compacting concrete surface finish. Adv Eng Softw 64:47–61

    Article  Google Scholar 

  14. Ofuyatan OM, Agbawhe OB, Omole DO, Igwegbe CA, Ighalo JO (2022) RSM and ANN modelling of the mechanical properties of self-compacting concrete with silica fume and plastic waste as partial constituent replacement. Clean. Mater 4:100065. https://doi.org/10.1016/j.clema.2022.100065

    Article  CAS  Google Scholar 

  15. Siddique R, Aggarwal P, Aggarwal Y (2011) Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Adv Eng Softw 42(10):780–786. https://doi.org/10.1016/j.advengsoft.2011.05.016

    Article  Google Scholar 

  16. Belalia Douma O, Boukhatem B, Ghrici M, Tagnit-Hamou A (2017) Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Comput Appl 28:707–718. https://doi.org/10.1007/s00521-016-2368-7

    Article  Google Scholar 

  17. Neter J (1983) Applied Linear Regression Models.pdf. p. 561

  18. Turk K, Karatas M, Gonen T (2013) Effect of fly ash and silica fume on compressive strength, sorptivity and carbonation of SCC. KSCE J Civ Eng 17(1):202–209. https://doi.org/10.1007/s12205-013-1680-3

    Article  Google Scholar 

  19. Yeh IC (1998) Modeling of strength of high-performance concrete using artificial neural networks. Cem Concr Res 28(12):1797–1808. https://doi.org/10.1016/S0008-8846(98)00165-3

    Article  CAS  Google Scholar 

  20. Oreta AWC (2004) Simulating size effect on shear strength of RC beams without stirrups using neural networks. Eng Struct 26(5):681–691. https://doi.org/10.1016/j.engstruct.2004.01.009

    Article  Google Scholar 

  21. Dias WPS, Pooliyadda SP (2001) Neural networks for predicting properties of concretes with admixtures. Constr Build Mater 15(7):371–379. https://doi.org/10.1016/S0950-0618(01)00006-X

    Article  Google Scholar 

  22. Öztaş A, Pala M, Özbay E, Kanca E, Çaǧlar N, Bhatti MA (2006) Predicting the compressive strength and slump of high strength concrete using neural network. Constr Build Mater 20(9):769–775. https://doi.org/10.1016/j.conbuildmat.2005.01.054

    Article  Google Scholar 

  23. Rachmatullah MIC, Santoso J, Surendro K (2021) Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction. PeerJ Comput Sci 7:1–19. https://doi.org/10.7717/PEERJ-CS.724

    Article  Google Scholar 

  24. Alshihri MM, Azmy AM, El-Bisy MS (2009) Neural networks for predicting compressive strength of structural light weight concrete. Constr Build Mater 23(6):2214–2219. https://doi.org/10.1016/j.conbuildmat.2008.12.003

    Article  Google Scholar 

  25. I. G. and Y. B. and Courville A (2016) Deep Learning, 29:7553. [Online]. Available: http://deeplearning.net/

  26. Harith IK, Hassan MS, Hasan SS (2022) Liquid nitrogen effect on the fresh concrete properties in hot weathering concrete. Innov Infrastruct Solut 7:1. https://doi.org/10.1007/s41062-021-00731-6

    Article  Google Scholar 

  27. Harith IK, Hussein MJ, Hashim MS (2022) Optimization of the synergistic effect of micro silica and fly ash on the behavior of concrete using response surface method. Open Eng 12(1):923–932. https://doi.org/10.1515/eng-2022-0332

    Article  CAS  Google Scholar 

  28. Nadir W, Harith IK, Ali AY (2022) Optimization of ultra-high-performance concrete properties cured with ponding water. Int J Sustain Build Technol Urban Dev 13(4):454–471. https://doi.org/10.22712/susb.20220033

    Article  Google Scholar 

  29. Harith IK, Hassan MS, Hasan SS, Majdi A (2023) Optimization of liquid nitrogen dosage to cool concrete made with hybrid blends of nanosilica and fly ash using response surface method. Innov Infrastruct Solut 8(5):1–15. https://doi.org/10.1007/s41062-023-01107-8

    Article  Google Scholar 

  30. Harith IK (2023) Optimization of quaternary blended cement for eco-sustainable concrete mixes using response surface methodology. Arab J Sci Eng. https://doi.org/10.1007/s13369-023-08071-6

    Article  Google Scholar 

  31. Han H, Yu R, Li B, Zhang Y, Wang W, Chen X (2019) Multi-objective optimization of corrugated tube with loose-fit twisted tape using RSM and NSGA-II. Int J Heat Mass Transf 131:781–794

    Article  Google Scholar 

  32. Aziminezhad M, Mahdikhani M, Memarpour MM (2018) RSM-based modeling and optimization of self-consolidating mortar to predict acceptable ranges of rheological properties. Constr Build Mater 189:1200–1213

    Article  CAS  Google Scholar 

  33. Gavin HP (2019) The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems. Duke Univ. pp. 1–19, [Online]. Available: http://people.duke.edu/~hpgavin/ce281/lm.pdf

  34. Bai Y, Chen M, Zhou P, Zhao T, Lee JD, Kakade S (2021) How important is the train-validation split in meta-learning ?

  35. Xu Y, Goodacre R (2018) On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J Anal Test 2(3):249–262. https://doi.org/10.1007/s41664-018-0068-2

    Article  PubMed  PubMed Central  Google Scholar 

  36. Dinakar P, Kartik Reddy M, Sharma M (2013) Behaviour of self compacting concrete using Portland pozzolana cement with different levels of fly ash. Mater Des 46:609–616. https://doi.org/10.1016/j.matdes.2012.11.015

    Article  CAS  Google Scholar 

  37. Leung HY, Kim J, Nadeem A, Jaganathan J, Anwar MP (2016) Sorptivity of self-compacting concrete containing fly ash and silica fume. Constr Build Mater 113:369–375. https://doi.org/10.1016/j.conbuildmat.2016.03.071

    Article  CAS  Google Scholar 

  38. Naik TR, Kumar R, Ramme BW, Canpolat F (2012) Development of high-strength, economical self-consolidating concrete. Constr Build Mater 30:463–469. https://doi.org/10.1016/j.conbuildmat.2011.12.025

    Article  Google Scholar 

  39. Zhu W, Bartos PJM (2003) Permeation properties of self-compacting concrete. Cem Concr Res 33(6):921–926. https://doi.org/10.1016/S0008-8846(02)01090-6

    Article  CAS  Google Scholar 

Download references

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Authors and Affiliations

  1. Civil Engineering Department, College of Engineering, Al-Qasim Green University, Babylon, 51013, Iraq

    Iman Kattoof Harith, Zainab Hashim Abbas & Mustafa Kareem Hamzah

  2. Department of Building and Construction Engineering, Technalogies, Al-Mustaqbal University, Hillah, Iraq

    Mohammed L. Hussien

Authors
  1. Iman Kattoof Harith
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  2. Zainab Hashim Abbas
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  3. Mustafa Kareem Hamzah
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  4. Mohammed L. Hussien
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Correspondence to Iman Kattoof Harith.

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Appendix A: Experimental database of self-compacting concrete

Appendix A: Experimental database of self-compacting concrete

See Table 7.

Table 7 Inputs and output parameters of (6-7-1) ANN and HR models
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Harith, I.K., Abbas, Z.H., Hamzah, M.K. et al. Comparison of artificial neural network and hierarchical regression in prediction compressive strength of self-compacting concrete with fly ash. Innov. Infrastruct. Solut. 9, 62 (2024). https://doi.org/10.1007/s41062-024-01367-y

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