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Foretelling the compressive strength of concrete using twin support vector regression

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Abstract

Characteristic compressive strength is a key and crucial physical attribute of concrete used in various design standards and rules. In this study, twin support vector regression (TSVR) is employed to foretell the concrete compressive strength (CCS) of high strength concrete (HSC). TSVR is a relatively new method that has shown strong generalization performance and rapid learning speed in many regression applications. The TSVR algorithm was constructed using datasets from the existing literature. Its outputs were then compared with those of preexisting models employed to foretell the strength of HSC. A total of 324 datasets from previous studies were used to train the models. The input variables employed for predicting CCS include superplasticizer (SU), fine aggregate (FA), coarse aggregate (CA), water (W), and cement (C). TSVR demonstrates commendable performance when compared to various other models, including artificial neural network (ANN), bagging regression trees (BRT), fuzzy polynomial neural networks (FPNN), genetic operation trees (GOT), neural-fuzzy inference system (NFIS), support vector machine (SVM) and others. Performance parameters, namely the coefficient of determination (R2), coefficient of correlation (R), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), indicate that the TSVR algorithm is highly capable of accurately foretelling the CCS.

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References

  1. Shen D et al (2022) Influence of loading ages on the early age tensile creep of high-strength concrete modified with superabsorbent polymers. J Mater Civ Eng 34(5):04022064

    Article  Google Scholar 

  2. Oneschkow N, Tim T (2022) Influence of the composition of high-strength concrete and mortar on the compressive fatigue behaviour. Mater Struct 55(2):1–21

    Article  Google Scholar 

  3. Al-Shamiri AK et al (2019) Modeling the compressive strength of high-strength concrete: an extreme learning approach. Constr Build Mater 208:204–219

    Article  Google Scholar 

  4. Öztaş A, Pala M, Özbay E, Kanca E, Çagˇlar N, Bhatti MA (2006) Predicting the compressive strength and slump of high strength concrete using neural network. Constr Build Mater 20(9):769–775

    Article  Google Scholar 

  5. Ashour SA (2000) Effect of compressive strength and tensile reinforcement ratio on flexural behavior of high-strength concrete beams. Eng Struct 22(5):413–423

    Article  Google Scholar 

  6. Hameed MM et al (2021) Prediction of high-strength concrete: high-order response surface methodology modeling approach. Eng Comput 38:1–14

    Google Scholar 

  7. Hadzima-Nyarko M et al (2020) Machine learning approaches for estimation of compressive strength of concrete. Eur Phys J Plus 135(8):682

    Article  Google Scholar 

  8. Chopra P et al (2018) Comparison of machine learning techniques for the prediction of compressive strength of concrete. Adv Civil Eng 2018:1

    Google Scholar 

  9. Silva PFS, Gray FM, Vanderci FA (2020) Machine learning techniques to predict the compressive strength of concrete. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería. https://doi.org/10.23967/j.rimni.2020.09.008

    Article  Google Scholar 

  10. Andalib A, Babak A, Alireza L (2022) Grey Wolf optimizer-based ANNs to predict the compressive strength of self-compacting concrete. Appl Comput Intell Soft Comput 2022:1

    Google Scholar 

  11. Hosein Ghanemi A, Amir T (2022) Use of different hyperparameter optimization algorithms in ANN for predicting the compressive strength of concrete containing calcined clay. Pract Period Struct Des Constr 27(2):04022002

    Article  Google Scholar 

  12. Ziolkowski P, Maciej N (2019) Machine learning techniques in concrete mix design. Materials 12(8):1256

    Article  Google Scholar 

  13. Candelaria MDE, Seong-Hoon K, Kang-Seok L (2022) Prediction of compressive strength of partially saturated concrete using machine learning methods. Materials 15(5):1662

    Article  Google Scholar 

  14. Almohammed F et al (2022) Assessment of soft computing techniques for the prediction of compressive strength of bacterial concrete. Materials 15(2):489

    Article  Google Scholar 

  15. Reuter U, Ahmad S, Dirk SR (2018) A comparative study of machine learning approaches for modeling concrete failure surfaces. Adv Eng Softw 116:67–79

    Article  Google Scholar 

  16. Alghamdi SJ (2022) Classifying high strength concrete mix design methods using decision trees. Materials 15(5):1950

    Article  Google Scholar 

  17. Paixão RCF da et al (2022) Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization. Revista IBRACON de Estruturas e Materiais

  18. Shariati M et al (2021) Assessment of longstanding effects of fly ash and silica fume on the compressive strength of concrete using extreme learning machine and artificial neural network. J Adv Eng Comput 5(1):50–74

    Article  Google Scholar 

  19. Shariati M et al (2020) A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement. Eng Comput 38:1–23

    Google Scholar 

  20. Kumar A et al (2022) Compressive strength prediction of lightweight concrete: machine learning models. Sustainability 14(4):2404

    Article  MathSciNet  Google Scholar 

  21. Bakouregui AS et al (2021) Explainable extreme gradient boosting tree-based prediction of load-carrying capacity of FRP-RC columns. Eng Struct 245:112836

    Article  Google Scholar 

  22. Nguyen HD, Gia TT, Myoungsu S (2021) Development of extreme gradient boosting model for prediction of punching shear resistance of r/c interior slabs. Eng Struct 235:112067

    Article  Google Scholar 

  23. Cui L et al (2021) Application of extreme gradient boosting based on grey relation analysis for prediction of compressive strength of concrete. Adv Civil Eng 2021:1

    Google Scholar 

  24. Khadem F et al (2017) Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front Struct Civ Eng 11(1):90–99

    Article  Google Scholar 

  25. Khademi F, Mahmoud A, Sayed MJ (2015) Prediction of compressive strength of concrete by data-driven models. I-Manager’s J Civ Eng 5:16–23

    Article  Google Scholar 

  26. Khademi F et al (2016) Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. Int J Sustain Built Environ 5(2):355–369

    Article  Google Scholar 

  27. Tang F, Yanqi W, Yisong Z (2022) Hybridizing grid search and support vector regression to predict the compressive strength of fly ash concrete. Adv Civil Eng 2022:1

    Google Scholar 

  28. Sun J et al (2019) Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression. Constr Build Mater 207:440–449

    Article  Google Scholar 

  29. Jiang P, Suzuki H, Obi T (2023) XAI-based cross-ensemble feature ranking methodology for machine learning models. Int J Inf Technol 15(4):1759–1768

    Google Scholar 

  30. Rakhee HMN, Bansal S (2023) Seasonal temperature forecasting using genetically tuned artificial neural network. Int J Inf Technol 16:1–5

    Google Scholar 

  31. Swathi T, Sudha S (2023) Crop classification and prediction based on soil nutrition using machine learning methods. Int J Inf Technol 15(6):2951–2960

    Google Scholar 

  32. Reyaz N, Ahamad G, Khan NJ, Naseem M, Ali J (2024) SVMCTI: support vector machine-based cricket talent identification model. Int J Inf Technol 9:1–14

    Google Scholar 

  33. Nidhi N, Lobiyal DK (2022) Traffic flow prediction using support vector regression. Int J Inf Technol 14(2):619–626

    Google Scholar 

  34. Kasperkiewicz J, Janusz R, Artur D (1995) HPC strength prediction using artificial neural network. J Comput Civ Eng 9(4):279–284

    Article  Google Scholar 

  35. Yeh IC (1998) Modeling of strength of high-performance concrete using artificial neural networks. Cem Concr Res 28(12):1797–1808

    Article  Google Scholar 

  36. Yeh IC, Lien LC (2009) Knowledge discovery of concrete material using genetic operation trees. Expert Syst Appl 36(3):5807–5812

    Article  Google Scholar 

  37. Prasad BR, Eskandari H, Reddy BV (2009) Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Constr Build Mater 23(1):117–128

    Article  Google Scholar 

  38. Hoang ND, Pham AD, Nguyen QL, Pham QN (2016) Estimating compressive strength of high-performance concrete with Gaussian process regression model. Adv Civil Eng 2016:1

    Article  Google Scholar 

  39. Deepa C, SathiyaKumari K, Sudha VP (2010) Prediction of the compressive strength of high-performance concrete mix using tree-based modeling. Int J Comput Appl 6(5):18–24

    Google Scholar 

  40. Chou JS, Chiu CK, Farfoura M, Al-Taharwa I (2011) Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. J Comput Civ Eng 25(3):242–253

    Article  Google Scholar 

  41. Gupta R, Kewalramani MA, Goel A (2006) Prediction of concrete strength using neural-expert system. J Mater Civ Eng 18(3):462–466

    Article  Google Scholar 

  42. Pham AD, Hoang ND, Nguyen QT (2016) Predicting compressive strength of high-performance concrete using metaheuristic-optimized least squares support vector regression. J Comput Civ Eng 30(3):06015002

    Article  Google Scholar 

  43. Zarandi MF, Türksen IB, Sobhani J, Ramezanianpour AA (2008) Fuzzy polynomial neural networks for approximation of the compressive strength of concrete. Appl Soft Comput 8(1):488–498

    Article  Google Scholar 

  44. Ly H-B, Thuy-Anh N, Binh TP (2022) Investigation on factors affecting early strength of high-performance concrete by Gaussian process regression. PLoS ONE 17(1):e0262930

    Article  Google Scholar 

  45. Hazarika BB, Deepak G, Narayanan N (2022) Wavelet Kernel least square twin support vector regression for wind speed prediction. Environ Sci Pollut Res 29:1–17

    Article  Google Scholar 

  46. Peng X (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3):365–372

    Article  Google Scholar 

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Acknowledgements

The experimental data utilized in the development of the current study's models were obtained from publicly available literature sources. The study acknowledges and recognizes all the sources of the data utilized in the research.

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Authors and Affiliations

  1. Motilal Nehru National Institute of Technology, Allahabad, 211004, India

    Deepak Gupta

  2. National Institute of Technology, Jote, Arunachal Pradesh, 791113, India

    Saurabh Dubey & Mainak Mallik

Authors
  1. Deepak Gupta
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  2. Saurabh Dubey
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  3. Mainak Mallik
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Contributions

Role of Saurabh Dubey: Conceptualization, Formal analysis, Writing – editing. Role of Deepak Gupta: Investigation, Visualization, Reviewing and editing. Role of Mainak Mallik: Formal analysis, Validation.

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Correspondence to Saurabh Dubey.

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Gupta, D., Dubey, S. & Mallik, M. Foretelling the compressive strength of concrete using twin support vector regression. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01913-y

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  • DOI: https://doi.org/10.1007/s41870-024-01913-y

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