Skip to main content
Log in

A success history-based adaptive differential evolution optimized support vector regression for estimating plastic viscosity of fresh concrete

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Plastic viscosity is an important parameter of fresh concrete mixes. This research investigates a machine learning-based method for constructing a functional mapping between concrete mix properties and the plastic viscosity. The investigated machine learning method relies on the support vector regression (SVR) which is a robust method for nonlinear and multivariate function approximation. Moreover, the history-based adaptive differential evolution with linear population size reduction (L-SHADE) is employed to optimize the SVR model construction phase. Thus, the proposed method, named L-SHADE-SVR, is an integration of machine learning and metaheuristic optimization. To train and verify the L-SHADE-SVR model, a dataset consisting of 142 experimental tests was collected. Experimental results with repetitive phases of model training and testing reveal that the newly constructed model is capable of delivering highly accurate estimation of the plastic viscosity with mean absolute percentage error of 12% and coefficient of determination of 0.82. These outcomes are superior compared to the employed benchmark methods including artificial neural network, multivariate adaptive regression spline, and sequential piecewise multiple linear regression. Therefore, the L-SHADE-SVR model is a promising tool to assist construction engineers in estimating the plastic viscosity of fresh concrete mixes.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Accord (2019) Accord.NET Framework http://accord-framework.net/. Accessed 09 Aug 2019

  2. ASTM (2015) ASTM C143/C143M-15a, Standard test method for slump of hydraulic-cement concrete. ASTM International, West Conshohocken

    Google Scholar 

  3. 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 

  4. Behnood A, Verian KP, Modiri Gharehveran M (2015) Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength. Constr Build Mater 98:519–529. https://doi.org/10.1016/j.conbuildmat.2015.08.124

    Article  Google Scholar 

  5. Bhola P, Bhardwaj S (2019) Estimation of solar radiation using support vector regression. J Inf Optim Sci 40:339–350. https://doi.org/10.1080/02522667.2019.1578093

    Article  Google Scholar 

  6. Bishop CM (2011) Pattern recognition and machine learning (information science and statistics). Springer. ISBN-10: 0387310738

  7. Bolandi H, Banzhaf W, Lajnef N, Barri K, Alavi AH (2019) An intelligent model for the prediction of bond strength of FRP bars in concrete. Soft Comput Approach Technol 7:42

    Google Scholar 

  8. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Paper presented at the Proceedings of the fifth annual workshop on Computational learning theory, Pittsburgh, Pennsylvania, USA

  9. Bui D-K, Nguyen T, Chou J-S, Nguyen-Xuan H, Ngo TD (2018) 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. https://doi.org/10.1016/j.conbuildmat.2018.05.201

    Article  Google Scholar 

  10. Cao G, Zhang H, Tan Y, Wang J, Deng R, Xiao X, Wu B (2015) Study on the effect of coarse aggregate volume fraction on the flow behavior of fresh concrete via DEM. Proc Eng 102:1820–1826. https://doi.org/10.1016/j.proeng.2015.01.319

    Article  Google Scholar 

  11. Choi MS, Kim YJ, Jang KP, Kwon SH (2014) Effect of the coarse aggregate size on pipe flow of pumped concrete. Constr Build Mater 66:723–730. https://doi.org/10.1016/j.conbuildmat.2014.06.027

    Article  Google Scholar 

  12. Chou J-S, Truong TTH (2019) Sliding-window metaheuristic optimization-based forecast system for foreign exchange analysis. Soft Comput. https://doi.org/10.1007/s00500-019-03863-1

    Article  Google Scholar 

  13. Deng F, He Y, Zhou S, Yu Y, Cheng H, Wu X (2018) Compressive strength prediction of recycled concrete based on deep learning. Constr Build Mater 175:562–569. https://doi.org/10.1016/j.conbuildmat.2018.04.169

    Article  Google Scholar 

  14. Denis Kaplan FDL, Thierry S (2005) Avoidance of blockages in concrete pumping process. Mater J 1:102. https://doi.org/10.14359/14446

  15. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1996) Support vector regression machines. In: Paper presented at the proceedings of the 9th international conference on neural information processing systems, Denver, Colorado

  16. EN (2011) EN 197-1:2011, Cement—Part 1: Composition, specifications and conformity criteria for common cements European Standard

  17. Etedali S, Mollayi N (2018) Cuckoo search-based least squares support vector machine models for optimum tuning of tuned mass dampers. Int J Struct Stab Dyn 18:1850028. https://doi.org/10.1142/s0219455418500281

    Article  MathSciNet  Google Scholar 

  18. Feys D, Cepuritis R, Jacobsen S, Lesage K, Secrieru E, Yahia A (2017) Measuring rheological properties of cement pastes: most common techniques. Proced Chall RILEM Tech Lett. https://doi.org/10.21809/rilemtechlett.2017.43

    Article  Google Scholar 

  19. Freund RJ, Wilson WJ, Sa P (2006) Regression analysis: statistical modeling of a response variable. Academic Press

  20. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–67

    MathSciNet  MATH  Google Scholar 

  21. Friedman JH, Roosen CB (1995) An introduction to multivariate adaptive regression splines. Stat Methods Med Res 4:197–217. https://doi.org/10.1177/096228029500400303

    Article  Google Scholar 

  22. Gholampour A, Mansouri I, Kisi O, Ozbakkaloglu T (2018) Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3630-y

  23. 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. https://doi.org/10.1016/j.jclepro.2017.11.186

    Article  Google Scholar 

  24. 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 

  25. Golafshani EM, Behnood A, Arashpour M (2020) Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. Constr Build Mater 232:117266. https://doi.org/10.1016/j.conbuildmat.2019.117266

  26. Goudos SK, Tsoulos GV, Athanasiadou G, Batistatos MC, Zarbouti D, Psannis KE (2019) Artificial neural network optimal modeling and optimization of UAV measurements for mobile communications using the L-SHADE algorithm. IEEE Trans Antennas Propag 67:4022–4031. https://doi.org/10.1109/TAP.2019.2905665

    Article  Google Scholar 

  27. Guo S, Tsai JS, Yang C, Hsu P (2015) A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In: 2015 IEEE congress on evolutionary computation (CEC), 25–28 May 2015, pp 1003–1010. https://doi.org/10.1109/cec.2015.7256999

  28. Hagan MT, Demuth HB, Beale MH, Jesús OD (2014) Neural network design, 2nd edn. Martin Hagan. ISBN-10: 0971732116,

  29. Hoang N-D (2019) Estimating punching shear capacity of steel fibre reinforced concrete slabs using sequential piecewise multiple linear regression and artificial neural network. Measurement 137:58–70. https://doi.org/10.1016/j.measurement.2019.01.035

    Article  Google Scholar 

  30. Hoang N-D, Chen C-T, Liao K-W (2017) Prediction of chloride diffusion in cement mortar using multi-gene genetic programming and multivariate adaptive regression splines. Measurement 112:141–149. https://doi.org/10.1016/j.measurement.2017.08.031

    Article  Google Scholar 

  31. Hoang N-D, Tran X-L, Nguyen H (2019) Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04258-x

    Article  Google Scholar 

  32. Hocine A (2018) Compressive strength prediction of limestone filler concrete using artificial neural networks. Adv Comput Des 3(3):289–302. https://doi.org/10.12989/acd.2018.3.3.289

  33. Hoła J, Sadowski Ł, Reiner J, Stach S (2015) Usefulness of 3D surface roughness parameters for nondestructive evaluation of pull-off adhesion of concrete layers. Constr Build Mater 84:111–120. https://doi.org/10.1016/j.conbuildmat.2015.03.014

  34. Jang KP, Kwon SH, Choi MS, Kim YJ, Park CK, Shah SP (2018) Experimental observation on variation of rheological properties during concrete pumping. Int J Concr Struct Mater 12:79. https://doi.org/10.1186/s40069-018-0310-3

    Article  Google Scholar 

  35. Jekabsons G (2016) ARESLab: adaptive regression splines toolbox for Matlab/Octave Technical report, Riga Technical University. http://www.csrtulv/jekabsons/

  36. Kim P (2017) MatLab deep learning with machine learning, neural networks and artificial intelligence. Press

  37. Kwon SH, Jang KP, Kim JH, Shah SP (2016) State of the art on prediction of concrete pumping. Int J Concr Struct Mater 10:75–85. https://doi.org/10.1007/s40069-016-0150-y

    Article  Google Scholar 

  38. Mai C-T, Kadri E-H, Ngo T-T, Kaci A, Riche M (2014) Estimation of the pumping pressure from concrete composition based on the identified tribological parameters. Adv Mater Sci Eng 2014:18. https://doi.org/10.1155/2014/503850

    Article  Google Scholar 

  39. Mechtcherine V, Nerella VN, Kasten K (2014) Testing pumpability of concrete using sliding pipe rheometer. Constr Build Mater 53:312–323. https://doi.org/10.1016/j.conbuildmat.2013.11.037

    Article  Google Scholar 

  40. Moazenzadeh R, Mohammadi B, Shamshirband S (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12:584–597. https://doi.org/10.1080/19942060.2018.1482476

    Article  Google Scholar 

  41. Ngo TT, Kadri EH, Bennacer R, Cussigh F (2010) Use of tribometer to estimate interface friction and concrete boundary layer composition during the fluid concrete pumping. Constr Build Mater 24:1253–1261. https://doi.org/10.1016/j.conbuildmat.2009.12.010

    Article  Google Scholar 

  42. Nhu V-H, Hoang N-D, Duong V-B, Vu H-D, Tien Bui D (2019) A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam). Eng Comput. https://doi.org/10.1007/s00366-019-00718-z

  43. Pham A-D, Hoang N-D, Nguyen Q-T (2016) Predicting compressive strength of high-performance concrete using metaheuristic-optimized least squares support vector regression. J Comput Civ Eng 30:06015002. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000506

    Article  Google Scholar 

  44. Piotrowski AP (2018) L-SHADE optimization algorithms with population-wide inertia. Inf Sci 468:117–141. https://doi.org/10.1016/j.ins.2018.08.030

    Article  Google Scholar 

  45. Piotrowski AP, Napiorkowski JJ (2018) Step-by-step improvement of JADE and SHADE-based algorithms: success or failure? Swarm Evol Comput 43:88–108. https://doi.org/10.1016/j.swevo.2018.03.007

    Article  Google Scholar 

  46. Prayogo D, Susanto YTT (2018) Optimizing the prediction accuracy of friction capacity of driven piles in cohesive soil using a novel self-tuning least squares support vector machine. Adv Civ Eng 2018:9. https://doi.org/10.1155/2018/6490169

    Article  Google Scholar 

  47. Price K, Storn RM, Lampinen JA (2005) Differential evolution—a practical approach to global optimization. Springer, Berlin. https://doi.org/10.1007/3-540-31306-0

  48. Sadowski Ł, Hoła J (2015) ANN modeling of pull-off adhesion of concrete layers. Adv Eng Softw 89:17–27. https://doi.org/10.1016/j.advengsoft.2015.06.013

    Article  Google Scholar 

  49. Sadowski Ł, Hoła J, Czarnecki S (2016) Non-destructive neural identification of the bond between concrete layers in existing elements. Constr Build Mater 127:49–58. https://doi.org/10.1016/j.conbuildmat.2016.09.146

    Article  Google Scholar 

  50. Sadowski Ł, Hoła J, Czarnecki S, Wang D (2018) Pull-off adhesion prediction of variable thick overlay to the substrate. Autom Constr 85:10–23. https://doi.org/10.1016/j.autcon.2017.10.001

    Article  Google Scholar 

  51. Sadowski Ł, Nikoo M, Nikoo M (2015) Principal component analysis combined with a self organization feature map to determine the pull-off adhesion between concrete layers. Constr Build Mater 78:386–396. https://doi.org/10.1016/j.conbuildmat.2015.01.034

    Article  Google Scholar 

  52. Sadowski Ł, Nikoo M, Shariq M, Joker E, Czarnecki S (2019) The nature-inspired metaheuristic method for predicting the creep strain of green concrete containing ground granulated blast furnace. Slag Mater 12:293

    Article  Google Scholar 

  53. Secrieru E, Cotardo D, Mechtcherine V, Lohaus L, Schröfl C, Begemann C (2018) Changes in concrete properties during pumping and formation of lubricating material under pressure. Cem Concr Res 108:129–139. https://doi.org/10.1016/j.cemconres.2018.03.018

    Article  Google Scholar 

  54. Seung Hee Kwon CKPJHJSDJ, Seung Hoon L (2013) Prediction of concrete pumping: Part I—development of new tribometer for analysis of lubricating layer. Mater J. https://doi.org/10.14359/51686332

  55. Soualhi H, Kadri E-H, Ngo T-T, Bouvet A, Cussigh F (2017) Design of portable rheometer with new vane geometry to estimate concrete rheological parameters. J Civ Eng Manag 23:347–355. https://doi.org/10.3846/13923730.2015.1128481

    Article  Google Scholar 

  56. Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: 2013 IEEE congress on evolutionary computation, 20–23 June 2013, pp 71–78. https://doi.org/10.1109/cec.2013.6557555

  57. Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC), 6–11 July 2014, pp 1658–1665. https://doi.org/10.1109/cec.2014.6900380

  58. TCVN (2006) TCVN 7570:2006—Aggregates for concrete and mortar—specifications ministry of construction. http://tieuchuanxaydung.com/tcvn-7570-2006. Accessed 07 Oct 2019

  59. TCVN (2009) TCVN 6260:2009, Portland blended cement—specifications ministry of construction. http://tieuchuanxaydung.com/tcvn-6260-2009/. Accessed 07 Oct 2019

  60. Tien Bui D, Tuan TA, Hoang N-D, Thanh NQ, Nguyen DB, Van Liem N, Pradhan B (2017) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14:447–458. https://doi.org/10.1007/s10346-016-0711-9

  61. Tran T-H, Hoang N-D (2016) Predicting colonization growth of algae on mortar surface with artificial neural network. J Comput Civ Eng 30:04016030. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000599

    Article  Google Scholar 

  62. Tran T-H, Hoang N-D (2017) Estimation of algal colonization growth on mortar surface using a hybridization of machine learning and metaheuristic optimization. Sādhanā 42:929–939. https://doi.org/10.1007/s12046-017-0652-6

    Article  Google Scholar 

  63. Tuba E, Tuba M, Simian D Adjusted bat algorithm for tuning of support vector machine parameters. In: 2016 IEEE congress on evolutionary computation (CEC), 24–29 July 2016, pp 2225–2232. https://doi.org/10.1109/cec.2016.7744063

  64. Wang H, Xu D (2017) Parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function. J Control Sci Eng 2017:12. https://doi.org/10.1155/2017/3614790

    Article  MathSciNet  MATH  Google Scholar 

  65. Yücel KT (2012) Examination of behavior of fresh concrete under pressure. Int J Thermophys 33:885–894. https://doi.org/10.1007/s10765-012-1180-6

    Article  Google Scholar 

  66. Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13:945–958

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant number 107.01-2016.17.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hoang Nhat-Duc.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table 4.

Table 4 The experimental dataset

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, TD., Tran, TH., Nguyen, H. et al. A success history-based adaptive differential evolution optimized support vector regression for estimating plastic viscosity of fresh concrete. Engineering with Computers 37, 1485–1498 (2021). https://doi.org/10.1007/s00366-019-00899-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-019-00899-7

Keywords

Navigation