Journal of Applied Science and Engineering

Published by Tamkang University Press


Impact Factor



Huifang Chen and Lingyang LiThis email address is being protected from spambots. You need JavaScript enabled to view it.

Cangzhou Jiaotong College, Cangzhou 061100, China


Received: July 6, 2022
Accepted: September 15, 2022
Publication Date: November 15, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||  


Concrete is an important factor in construction. It is necessary to use a special structure using high-performance concrete. High-performance concrete (HPC) is concrete with high strength and performance. Predicting the compressive strength (CS) of concrete is done in the laboratory, so it takes time. To save time, use a software model to predict the compressive strength of concrete. Support Vector Regression (SVR) is an innovative model for predicting most models, and SVR belongs to the Support Vector Machine (SVM) regression model. This model uses dual Improved Grey Wolf optimizer (I_GWO) and Dragonfly optimization algorithms (DA) for better output. Both of these are innovations in hidden layer optimization. Models and algorithms are specified in combination with SVR-GWO and SVR-DA. This modeling is error-prone and contains RMSE, MAPE, MDAPE, WAPE, and SMAPE and is shown as a parameter and the amount of R2 most specified in any hybrid model.

Keywords: High-performance-concrete; compressive strength; Improved Grey Wolf algorithm; Dragonfly optimization algorithm; support vector machine


  1. [1] H. Cheng, S. Kitchen, and G. Daniels, (2022) “Novel hybrid radial based neural network model on predicting the compressive strength of long-term HPC concrete" Advances in Engineering and Intelligence Systems 1(02): DOI: 10.22034/AEIS.2022.340732.1012..
  2. [2] S. Fallah and M. Nematzadeh, (2017) “Mechanical properties and durability of high-strength concrete containing macro-polymeric and polypropylene fibers with nano-silica and silica fume" Construction and Building Materials 132: 170–187. DOI: 10.1016/j.conbuildmat.2016.11.100..
  3. [3] M. Nematzadeh and S. Fallah-Valukolaee, (2017) “Effectiveness of fibers and binders in high-strength concrete under chemical corrosion" Structural Engineering and Mechanics 64(2): 243–257. DOI: 10.12989/sem.2017.64.2.243.
  4. [4] M. Nematzadeh, S.-A. Hosseini, and T. Ozbakkaloglu, (2021) “The combined effect of crumb rubber aggregates and steel fibers on shear behavior of GFRP bar-reinforced high-strength concrete beams" Journal of Building Engineering 44: 102981. DOI:10.1016/j.jobe.2021.102981..
  5. [5] P. Zhang, Z. Gao, J. Wang, J. Guo, S. Hu, and Y. Ling, (2020) “Properties of fresh and hardened fly ash/slag based geopolymer concrete: A review" Journal of Cleaner Production 270: 122389. DOI: 10.1016/j . jclepro.2020.122389.
  6. [6] P. Zhang, C. Liu, and Q. Li, (2011) “Application of gray relational analysis for chloride permeability and freezethaw resistance of high-performance concrete containing nanoparticles" Journal of Materials in Civil Engineering 23(12): 1760–1763. DOI: 10.1061/(ASCE)MT.1943-5533.0000332.
  7. [7] S. Supit and F. Shaikh, (2015) “Durability properties of high volume fly ash concrete containing nano-silica" Materials and Structures 48(8): 2431–2445. DOI: 10.1617/s11527-014-0329-0.
  8. [8] D. Pedro, J. Brito, and L. Evangelista, (2018) “Durability performance of high-performance concrete made with recycled aggregates, fly ash and densified silica fume" Cement and Concrete Composites 93: 63–74. DOI:10.1016/j.cemconcomp.2018.07.002.
  9. [9] P. Smarzewski, (2019) “Influence of silica fume on mechanical and fracture properties of high performance concrete" Procedia Structural Integrity 17: 5–12. DOI:10.1016/j.prostr.2019.08.002.
  10. [10] S. Ahmad and A. Umar, (2018) “Rheological and mechanical properties of self-compacting concrete with glass and polyvinyl alcohol fibres" Journal of Building Engineering 17: 65–74.
  11. [11] H. Shi, B. Xu, and X. Zhou, (2009) “Influence of mineral admixtures on compressive strength, gas permeability and carbonation of high performance concrete" Construction and Building Materials 23(5): 1980–1985.
  12. [12] R. Duval and E. Kadri, (1998) “Influence of Silica Fume on the Workability and the Compressive Strength of High-Performance Concretes" Cement and Concrete Research 28(4): 533–547. DOI: 10.1016/S0008-8846(98)00010-6..
  13. [13] P. Zhang, Z. Gao, J.Wang, and K. Wang, (2021) “Numerical modeling of rebar-matrix bond behaviors of nano-SiO2 and PVA fiber reinforced geopolymer composites" Ceramics International 47(8): 11727–11737.
  14. [14] F. Khademi, M. Akbari, and S. Jamal, (2015) “Prediction of compressive strength of concrete by data-driven models" I-manager’s Journal on Civil Engineering 5: 16–23.
  15. [15] D. Armaghani and P. Asteris, (2021) “A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength" Neural Computing and Applications 33(9): 4501–4532.
  16. [16] O. Avci, O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj, and D. Inman, (2021) “A review of vibration based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications" Mechanical Systems and Signal Processing 147: 107077.
  17. [17] M. Nematzadeh, A. Shahmansouri, and R. Zabihi. “Innovative models for predicting post-fire bond behavior of steel rebar embedded in steel fiber reinforced rubberized concrete using soft computing methods”. en. In: Structures. 31. 2021, 1141–1162.
  18. [18] P. Asteris, M. Apostolopoulou, A. Skentou, and A. Moropoulou, (2019) “Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars" Computers and Concrete 24(4): 329–345. DOI: 10.12989/cac.2019.24.4.329.
  19. [19] N.-D. Hoang, (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.
  20. [20] V. Vapnik, S. Golowich, and A. Smola, (1996) “Support vector method for function approximation, regression estimation and signal processing" Advances in Neural Information Processing Systems 9:
  21. [21] L. Wang. Support vector machines: theory and applications. en. 177. Springer Science & Business Media, 2005.
  22. [22] A. Smola and B. Schölkopf, (2004) “A tutorial on support vector regression" Statistics and Computing 14(3): 199–222. DOI: 10.1023/B:STCO.0000035301.49549.88..
  23. [23] J. Platt, (1999) “Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods" Advances in Large-Margin Classifiers 10(3): 61–74.
  24. [24] K. Bennett and C. Campbell, (2000) “Support vector machines: hype or hallelujah?" ACM SIGKDD Explorations Newsletter 2(2): 1–13.
  25. [25] Z. Cai, (2019) “Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy" Expert Systems with Applications 138:112814.
  26. [26] S. Mirjalili, (2016) “Dragonfly algorithm: a new metaheuristic optimization technique for solving singleobjective, discrete, and multi-objective problems" Neural Computing and Applications 27(4): 1053–1073.
  27. [27] V. Suresh and S. Sreejith, (2017) “Generation dispatch of combined solar thermal systems using dragonfly algorithm" Computing 99(1): 59–80.
  28. [28] M. Alshinwan, (2021) “Dragonfly algorithm: a comprehensive survey of its results, variants, and applications" Multimedia Tools and Applications 80(10): 14979–15016.
  29. [29] S. Abe. Support vector machines for pattern classification. en. 2. Springer, 2005.
  30. [30] V. Vapnik. The nature of statistical learning theory. en. Springer science & business media, 2013.
  31. [31] R. Fletcher. Practical methods of optimization. en. John Wiley & Sons, 2013.
  32. [32] J. Sun, J. Zhang, Y. Gu, Y. Huang, Y. Sun, and G. Ma, (2019) “Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression" Construction and Building Materials 207: 440–449.
  33. [33] Y. Moodi, M. Ghasemi, and S. Mousavi, (2022) “Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods" Journal of Reinforced Plastics and Composites 41(3–4): 130–146. DOI: 10.1177/07316844211050168..
  34. [34] H. Chen, X. Li, Y. Wu, L. Zuo, M. Lu, and Y. Zhou, (2022) “Compressive strength prediction of high-strength concrete using long short-term memory and machine learning algorithms" Buildings 12(3): 302.



60th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.