Skip to main content
SpringerLink
Log in

A hybrid machine learning model to estimate self-compacting concrete compressive strength

  • Research Article
  • Published:
Frontiers of Structural and Civil Engineering Aims and scope Submit manuscript

Abstract

This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Neville A M, Adam M. Properties of Concrete. Pearson, 2011

  2. Kovler K, Roussel N. Properties of fresh and hardened concrete. Cement and Concrete Research, 2011, 41(7): 775–792

    Article  Google Scholar 

  3. Wiktor V, Jonkers H M. Quantification of crack-healing in novel bacteria-based self-healing concrete. Cement and Concrete Composites, 2011, 33(7): 763–770

    Article  Google Scholar 

  4. Kan A, Demirboğa R. A novel material for lightweight concrete production. Cement and Concrete Composites, 2009, 31(7): 489–495

    Article  Google Scholar 

  5. Houst Y F, Bowen P, Perche F, Kauppi A, Borget P, Galmiche L, Le Meins J F, Lafuma F, Flatt R J, Schober I, Banfill P F G, Swift D S, Myrvold B O, Petersen B G, Reknes K. Design and function of novel superplasticizers for more durable high performance concrete (superplast project). Cement and Concrete Research, 2008, 38(10): 1197–1209

    Article  Google Scholar 

  6. Okamura H. Self-compacting high-performance concrete. Concrete International, 1997, 19: 50–54

    Google Scholar 

  7. K. OZAWA. High-performance concrete based on the durability design of concrete structures. In: Proceedings of the Second East Asia-Pacific Conference on Structural Engineering and Construction. 1989

  8. Domone P L. A review of the hardened mechanical properties of self-compacting concrete. Cement and Concrete Composites, 2007, 29(1): 1–12

    Article  Google Scholar 

  9. Shi C, Wu Z, Lv K, Wu L. A review on mixture design methods for self-compacting concrete. Construction & Building Materials, 2015, 84: 387–398

    Article  Google Scholar 

  10. Okamura H, Ouchi M. Self-compacting concrete. Journal of Advanced Concrete Technology, 2003, 1(1): 5–15

    Article  Google Scholar 

  11. Domone P L. Self-compacting concrete: An analysis of 11 years of case studies. Cement and Concrete Composites, 2006, 28(2): 197–208

    Article  Google Scholar 

  12. Brouwers H J H, Radix H J. Self-compacting concrete: Theoretical and experimental study. Cement and Concrete Research, 2005, 35(11): 2116–2136

    Article  Google Scholar 

  13. Shi C, Yanzhong W. Mixture proportioning and properties of self-consolidating lightweight concrete containing glass powder. ACI Materials Journal, 2005, 102: 355–363

    Google Scholar 

  14. Haach V G, Vasconcelos G, Lourenço P B. Influence of aggregates grading and water/cement ratio in workability and hardened properties of mortars. Construction & Building Materials, 2011, 25(6): 2980–2987

    Article  Google Scholar 

  15. Molero M, Segura I, Izquierdo M A G, Fuente J V, Anaya J J. Sand/cement ratio evaluation on mortar using neural networks and ultrasonic transmission inspection. Ultrasonics, 2009, 49(2): 231–237

    Article  Google Scholar 

  16. Okamura H, Ozawa K, Ouchi M. Self-compacting concrete. Structural Concrete, 2000, 1(1): 3–17

    Article  Google Scholar 

  17. Esmaeilkhanian B, Khayat K H, Yahia A, Feys D. Effects of mix design parameters and rheological properties on dynamic stability of self-consolidating concrete. Cement and Concrete Composites, 2014, 54: 21–28

    Article  Google Scholar 

  18. Yaşar E, Erdoğan Y, Kılıç A. Effect of limestone aggregate type and water-cement ratio on concrete strength. Materials Letters, 2004, 58(5): 772–777

    Article  Google Scholar 

  19. Uysal M, Tanyildizi H. Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network. Construction & Building Materials, 2012, 27(1): 404–414

    Article  Google Scholar 

  20. Sathyan D, Anand K B, Prakash A J, Premjith B. Modeling the fresh and hardened stage properties of self-compacting concrete using random kitchen sink algorithm. International Journal of Concrete Structures and Materials, 2018, 12(1): 1–10

    Article  Google Scholar 

  21. Sonebi M. Applications of statistical models in proportioning medium-strength self-consolidating concrete. ACI Materials Journal, 2004, 101(5): 339–346

    Google Scholar 

  22. Sonebi M. Medium strength self-compacting concrete containing fly ash: Modelling using factorial experimental plans. Cement and Concrete Research, 2004, 34(7): 1199–1208

    Article  Google Scholar 

  23. Vu-Bac N, Lahmer T, Keitel H, Zhao J, Zhuang X, Rabczuk T. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84

    Article  Google Scholar 

  24. Vu-Bac N, Lahmer T, Zhang Y, Zhuang X, Rabczuk T. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs). Composites. Part B, Engineering, 2014, 59: 80–95

    Article  Google Scholar 

  25. Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535

    Article  Google Scholar 

  26. Vu-Bac N, Rafiee R, Zhuang X, Lahmer T, Rabczuk T. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites. Part B, Engineering, 2015, 68: 446–464

    Article  Google Scholar 

  27. Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59(2): 433–456

    Article  Google Scholar 

  28. Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790

    Article  MathSciNet  MATH  Google Scholar 

  29. Guo H, Zhuang X, Chen P, Alajlan N, Rabczuk T. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with Computers, 2022, 1–26

  30. Guo H, Zhuang X, Chen P, Alajlan N, Rabczuk T. Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis. Engineering with Computers, 2022, 1–22

  31. Liu B, Vu-Bac N, Zhuang X, Fu X, Rabczuk T. Stochastic full-range multiscale modeling of thermal conductivity of polymeric carbon nanotubes composites: A machine learning approach. Composite Structures, 2022, 289: 115393

    Article  Google Scholar 

  32. Liu B, Vu-Bac N, Rabczuk T. A stochastic multiscale method for the prediction of the thermal conductivity of polymer nanocomposites through hybrid machine learning algorithms. Composite Structures, 2021, 273: 114269

    Article  Google Scholar 

  33. Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1), 345–359

    Article  Google Scholar 

  34. Dao D V, Ly H B, Trinh S H, Le T T, Pham B T. Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials (Basel), 2019, 12(6): 983

    Article  Google Scholar 

  35. Ly H B, Pham B T, Dao D V, Le V M, Le L M, Le T T. Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials (Basel), 2019, 12(6): 3841

    Google Scholar 

  36. Le T H, Nguyen H L, Pham B T, Nguyen M H, Pham C T, Nguyen N L, Le T T, Ly H B. Artificial intelligence-based model for the prediction of dynamic modulus of stone mastic asphalt. Applied Sciences, 2020, 10(15), 5242.

    Article  Google Scholar 

  37. Sebastiá M, Fernández Olmo I, Irabien A. Neural network prediction of unconfined compressive strength of coal fly ash-cement mixtures. Cement and Concrete Research, 2003, 33(8): 1137–1146

    Article  Google Scholar 

  38. Lee S C. Prediction of concrete strength using artificial neural networks. Engineering Structures, 2003, 25(7): 849–857

    Article  Google Scholar 

  39. Ly H B, Nguyen M H, Pham B T. Metaheuristic optimization of Levenberg-Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Computing and Applications, 2021, 33(24): 17331–17351

    Article  Google Scholar 

  40. Yeh I C. Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research, 1998, 28(12): 1797–1808

    Article  Google Scholar 

  41. Belalia Douma O, Boukhatem B, Ghrici M, Tagnit-Hamou A. Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Computing & Applications, 2017, 28(S1): 707–718

    Article  Google Scholar 

  42. Siddique R, Aggarwal P, Aggarwal Y. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering Software, 2011, 42(10): 780–786

    Article  Google Scholar 

  43. Topçu İ B, Sarıdemir M. Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Construction & Building Materials, 2008, 22(4): 532–540

    Article  Google Scholar 

  44. Trtnik G, Kavčič F, Turk G. Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics, 2009, 49(1): 53–60

    Article  Google Scholar 

  45. Dias W P S, Pooliyadda S P. Neural networks for predicting properties of concretes with admixtures. Construction & Building Materials, 2001, 15(7): 371–379

    Article  Google Scholar 

  46. Pradhan B, Lee S. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 2010, 25(6): 747–759

    Article  Google Scholar 

  47. Nehdi M, Djebbar Y, Khan A. Neural network model for preformed-foam cellular concrete. Materials Journal, 2001, 98(5): 402–409

    Google Scholar 

  48. Delnavaz M, Ayati B, Ganjidoust H. Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN). Journal of Hazardous Materials, 2010, 179(1–3): 769–775

    Article  Google Scholar 

  49. Aiyer B G, Kim D, Karingattikkal N, Samui P, Rao P R. Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine. KSCE Journal of Civil Engineering, 2014, 18(6): 1753–1758

    Article  Google Scholar 

  50. Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69: 46–61

    Article  Google Scholar 

  51. Li B, Yan H, Zhang J, Zhou N. Compaction property prediction of mixed gangue backfill materials using hybrid intelligence models: A new approach. Construction & Building Materials, 2020, 247: 118633

    Article  Google Scholar 

  52. Behnood A, Golafshani E M. Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production, 2018, 202: 54–64

    Article  Google Scholar 

  53. Golafshani E M, Behnood A, Arashpour M. Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. Construction & Building Materials, 2020, 232: 117266

    Article  Google Scholar 

  54. Gettu R, Izquierdo J, Pcc G, Josa A. Development of high-strength self compacting concrete with fly ash: A four-step experimental methodology. In: Proceedings of 27th Conference on Our World in Concrete & Structures. Singapore CI-Premier Pte Ltd., 2002, 217–224

  55. Patel R. Development of statistical models to simulate and optimize self-consolidating concrete mixes incorporating high volumes of fly ash. Thesis for the Master’s Degree. Toronto: Toronto Metropolitan University, 2004, 1802–1802

    Google Scholar 

  56. Uysal M, Yilmaz K. Effect of mineral admixtures on properties of self-compacting concrete. Cement and Concrete Composites, 2011, 33(7): 771–776

    Article  Google Scholar 

  57. Mahalingam B, Nagamani K. Effect of processed fly ash on fresh and hardened properties of self compacting concrete. International Journal of Earth Sciences and Engineering, 2011, 4: 930–940

    Google Scholar 

  58. Bingöl A F, Tohumcu İ. Effects of different curing regimes on the compressive strength properties of self compacting concrete incorporating fly ash and silica fume. Materials & Design, 2013, 51: 12–18

    Article  Google Scholar 

  59. Siddique R, Aggarwal P, Aggarwal Y. Influence of water/powder ratio on strength properties of self-compacting concrete containing coal fly ash and bottom ash. Construction & Building Materials, 2012, 29: 73–81

    Article  Google Scholar 

  60. Nepomuceno M C, Pereira-de-Oliveira L A, Lopes S M R. Methodology for the mix design of self-compacting concrete using different mineral additions in binary blends of powders. Construction & Building Materials, 2014, 64: 82–94

    Article  Google Scholar 

  61. Güneyisi E, Gesoğlu M, Özbay E. Strength and drying shrinkage properties of self-compacting concretes incorporating multi-system blended mineral admixtures. Construction & Building Materials, 2010, 24(10): 1878–1887

    Article  Google Scholar 

  62. Krishnapal P, Yadav R K, Rajeev C. Strength characteristics of self compacting concrete containing fly ash. Research Journal of Engineering Sciences, 2013, 9472: 1–5

    Google Scholar 

  63. Dhiyaneshwaran S, Ramanathan P, Baskar I, Venkatasubramani R. Study on durability characteristics of self-compacting concrete with fly ash. Jordan Journal of Civil Engineering, 2013, 7: 342–352

    Google Scholar 

  64. Şahmaran M, Yaman İ Ö, Tokyay M. Transport and mechanical properties of self consolidating concrete with high volume fly ash. Cement and Concrete Composites, 2009, 31(2): 99–106

    Article  Google Scholar 

  65. Witten I H, Frank E. Data mining: Practical machine learning tools and techniques with Java implementations. SIGMOD Record, 2002, 31(1): 76–77

    Article  Google Scholar 

  66. Menard S. Coefficients of determination for multiple logistic regression analysis. The American Statistician, 2000, 54(1), 17–24

    Google Scholar 

  67. Willmott C, Matsuura K. Advantages of the mean absolute error (mae) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 2005, 30: 79–82

    Article  Google Scholar 

  68. Friedman J H. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 2001, 29(5): 1189–1232

    Article  MathSciNet  MATH  Google Scholar 

  69. Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 1989, 2(4): 303–314

    Article  MathSciNet  MATH  Google Scholar 

  70. Bounds L, Mathew W. A multilayer perceptron network for the diagnosis of low back pain. In: Proceedings of IEEE 1988 International Conference on Neural Networks. New York: IEEE, 1988, 481–489

    Google Scholar 

  71. Mohamad E T, Faradonbeh R S, Armaghani D J, Monjezi M, Majid M Z A. An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Computing & Applications, 2017, 28(S1): 393–406

    Article  Google Scholar 

  72. Stern F, Muste M, Beninati M, Eichinger W E. Summary of Experimental Uncertainty Assessment Methodology with Example. No. 406. IIHR Report. 1999

  73. Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

    Article  Google Scholar 

  74. Vu-Bac N, Zhuang X, Rabczuk T. Uncertainty quantification for mechanical properties of polyethylene based on fully atomistic model. Materials (Basel), 2019, 12(21): 3613

    Article  Google Scholar 

  75. Liu B, Vu-Bac N, Zhuang X, Rabczuk T. Stochastic multiscale modeling of heat conductivity of Polymeric clay nanocomposites. Mechanics of Materials, 2020, 142: 103280

    Article  Google Scholar 

  76. Ly H B, Le L M, Duong H T, Nguyen T C, Pham T A, Le T T, Le V M, Nguyen-Ngoc L, Pham B T. Hybrid artificial intelligence approaches for predicting critical buckling load of structural members under compression considering the influence of initial geometric imperfections. Applied Sciences (Basel, Switzerland), 2019, 9(11): 2258

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil Engineering Department, University of Transport Technology, Hanoi, 100000, Vietnam

    Hai-Bang Ly, Thuy-Anh Nguyen, Binh Thai Pham & May Huu Nguyen

  2. Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima, 739-8527, Japan

    May Huu Nguyen

Authors
  1. Hai-Bang Ly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thuy-Anh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Binh Thai Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. May Huu Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to May Huu Nguyen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ly, HB., Nguyen, TA., Pham, B.T. et al. A hybrid machine learning model to estimate self-compacting concrete compressive strength. Front. Struct. Civ. Eng. 16, 990–1002 (2022). https://doi.org/10.1007/s11709-022-0864-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11709-022-0864-7

Keywords

Search

Navigation