Lijun Wang 1 and Pengcheng Yang2  

1School of Zhejiang Guangsha Vocational and Technical University of construction; Dongyang Zhejiang 322100 China
2S&C Design Group; Hangzhou Zhejiang 310000 China


Received: August 4, 2022
Accepted: October 2, 2022
Publication Date: December 14, 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: ||  

The compressive strength of ultra great workability concrete (UGWC) is a function of the kind, characteristics and quantities of its material components. Empirically gaining this relation sometimes needs the usage of intelligent algorithms to receive a simulative model that fits into experimental data records. In this study, the usefulness of developing hybridized regression analysis on UGWC was analyzed with the aim of reducing the consumed time and experimental efforts. To this aim, a dataset including 170 samples collected from published papers different hybridized support vector regression (SVR) analyses were produced, where the optimal values of determinant attributes of SVR were explored by metaheuristic optimization algorithms named particle swarm optimization (PSO), Cuckoo optimization algorithm (COA), and Bat algorithm (BAT). The performance evaluators demonstrate that all three hybridized SVR models have remarkable potential in compressive strength estimation of UGWC. The first rank belonged to the SVR-COA model, where it could gain the highest value of R2 and variance account factor (VAF) in both training (R2=0.9056 and VAF=90.17%) and validating section (R2=0.9208 and VAF=91.81%), and the lowest value of root mean square error, and mean absolute error in both training and validating sections. Therefore, the hybridized SVR COA model could receive the proper accuracy in comparison with other models as well as literature.

Keywords: Ultra great workability concrete; Compressive strength; Prediction; SVR analysis; Optimization algorithms

  1. [1] M. Esmaeili Falak and R. Sarkhani Benemaran, (2022) “Investigating the stress-strain behavior of frozen clay using triaxial test" Journal of Structural and Construction Engineering:
  2. [2] A. Poorjafar, M. Esmaeili-Falak, and H. Katebi, (2021) “Pile-soil interaction determined by laterally loaded fixed head pile group" Geomechanics and Engineering 26(1): 13–25. DOI: 10.12989/gae.2021.26.1.013.
  3. [3] R. Sarkhani Benemaran, M. Esmaeili-Falak, and H. Katebi, (2021) “Physical and numerical modelling of pilestabilised saturated layered slopes" Proceedings of the Institution of Civil Engineers: Geotechnical Engineering: DOI: 10.1680/jgeen.20.00152.
  4. [4] N. Esmaeili-Choobar, M. Esmaeili-Falak, M. Roohi- Hir, and S. Keshtzad, (2013) “Evaluation of collapsibility potential at Talesh, Iran" Electronic Journal of Geotechnical Engineering 18 M: 2561–2573.[5] D.-Y. Yoo and N. Banthia, (2016) “Mechanical properties of ultra-high-performance fiber-reinforced concrete: A review" Cement and Concrete Composites 73: 267– 280. DOI: 10.1016/j.cemconcomp.2016.08.001.
  5. [5] D.-Y. Yoo and N. Banthia, (2016) “Mechanical properties of ultra-high-performance fiber-reinforced concrete: A review" Cement and Concrete Composites 73: 267–280. DOI: 10.1016/j.cemconcomp.2016.08.001.
  6. [6] M. Esmaeili-Falak and M. Hajialilue-Bonab, (2012) “Numerical studying the effects of gradient degree on slope stability analysis using limit equilibrium and finite element methods" Int J Acad Res 4(4): 216–22.
  7. [7] D. Wang, C. Shi, Z. Wu, J. Xiao, Z. Huang, and Z. Fang, (2015) “A review on ultra high performance concrete: Part II. Hydration, microstructure and properties" Construction and Building Materials 96: 368–377. DOI: 10.1016/j.conbuildmat.2015.08.095.
  8. [8] M. Esmaeili-Falak, H. Katebi, and A. Javadi, (2020) “Effect of freezing on stress-strain characteristics of granular and cohesive soils" Journal of Cold Regions Engineering 34(2): DOI: 10.1061/(ASCE)CR.1943-5495.0000205.
  9. [9] M. Esmaeili-Falak, H. Katebi, and A. Javadi, (2018) “Experimental study of the mechanical behavior of frozen soils - A case study of Tabriz Subway" Periodica Polytechnica Civil Engineering 62(1): 117–125. DOI: 10.3311/PPci.10960.
  10. [10] R. Yu, P. Spiesz, and H. Brouwers, (2014) “Mix design and properties assessment of Ultra-High Performance Fibre Reinforced Concrete (UHPFRC)" Cement and Concrete Research 56: 29–39. DOI: 10.1016/j.cemconres.2013.11.002.
  11. [11] C. Wang, C. Yang, F. Liu, C. Wan, and X. Pu, (2012) “Preparation of Ultra-High Performance Concrete with common technology and materials" Cement and Concrete Composites 34(4): 538–544. DOI: 10.1016/j .cemconcomp.2011.11.005.
  12. [12] R. Yu, P. Spiesz, and H. Brouwers, (2014) “Effect of nano-silica on the hydration and microstructure development of Ultra-High Performance Concrete (UHPC) with a low binder amount" Construction and Building Materials 65: 140–150. DOI: 10.1016/j.conbuildmat.2014.04.063.
  13. [13] M. Najafzadeh and G. Oliveto, (2022) “Scour Propagation Rates around Offshore Pipelines Exposed to Currents by Applying Data-Driven Models"Water (Switzerland)14(3): DOI: 10.3390/w14030493.
  14. [14] R. Sarkhani Benemaran, M. Esmaeili-Falak, and A. Javadi, (2022) “Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models" International Journal of Pavement Engineering: DOI: 10.1080/10298436.2022 .2095385.
  15. [15] H. Farhadi and M. Najafzadeh, (2021) “Flood risk mapping by remote sensing data and random forest technique" Water (Switzerland) 13(21): DOI: 10 . 3390/w13213115.
  16. [16] M. Najafzadeh and S. Niazmardi, (2021) “A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters" Natural Resources Research 30(5): 3761–3775. DOI: 10.1007/s11053-021-09895-5.
  17. [17] M. Esmaeili-Falak, H. Katebi, M. Vadiati, and J. Adamowski, (2019) “Predicting Triaxial Compressive Strength and Young’s Modulus of Frozen Sand Using Artificial Intelligence Methods" Journal of Cold Regions Engineering 33(3): DOI: 10.1061/(ASCE)CR.1943-5495.0000188.
  18. [18] M. Najafzadeh and A. Ghaemi, (2019) “Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods" Environmental Monitoring and Assessment 191(6): DOI: 10.1007/s10661-019-7446-8.
  19. [19] M. Najafzadeh and M. Zeinolabedini, (2019) “Prognostication of waste water treatment plant performance using efficient soft computing models: An environmental evaluation" Measurement: Journal of the International Measurement Confederation 138: 690–701. DOI: 10.1016/j.measurement.2019.02.014.
  20. [20] J. Yuan, M. Zhao, and M. Esmaeili-Falak, (2022) “A comparative study on predicting the rapid chloride permeability of self-compacting concrete using meta-heuristic algorithm and artificial intelligence techniques" Structural Concrete 23(2): 753–774. DOI: 10.1002/suco. 202100682.
  21. [21] D.-M. Ge, L.-C. Zhao, and M. Esmaeili-Falak, (2022) “Estimation of rapid chloride permeability of SCC using hyperparameters optimized random forest models" Journal of Sustainable Cement-Based Materials: DOI: 10.1080/21650373.2022.2093291.
  22. [22] T. Chen, X. Gao, and M. Ren, (2018) “Effects of autoclave curing and fly ash on mechanical properties of ultrahigh performance concrete" Construction and Building Materials 158: 864–872. DOI: 10.1016/j.conbuildmat.2017.10.074.
  23. [23] A. Arora, M. Aguayo, H. Hansen, C. Castro, E. Federspiel, B. Mobasher, and N. Neithalath, (2018) “Microstructural packing- and rheology-based binder selection and characterization for Ultra-high Performance Concrete (UHPC)" Cement and Concrete Research 103: 179–190. DOI: 10.1016/j.cemconres.2017.10.013.
  24. [24] X. Zhang, S. Zhao, Z. Liu, and F. Wang, (2019) “Utilization of steel slag in ultra-high performance concrete with enhanced eco-friendliness" Construction and Building Materials 214: 28–36. DOI: 10.1016/j.conbuildmat.2019.04.106.
  25. [25] A. Alsalman, C. N. Dang, and W. Micah Hale, (2017) “Development of ultra-high performance concrete with locally available materials" Construction and Building
    Materials 133: 135–145. DOI: 10.1016/j.conbuildmat.2016.12.040.
  26. [26] Z. Wu, C. Shi, K. H. Khayat, and L. Xie, (2018) “Effect of SCM and nano-particles on static and dynamic mechanical properties of UHPC" Construction and Building Materials 182: 118–125. DOI: 10.1016/j.conbuildmat.2018.06.126.
  27. [27] W. Zhu, L. Huang, L. Mao, and M. Esmaeili-Falak, (2021) “Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence-based algorithms" Structural Concrete: DOI: 10.1002/suco.202100656.
  28. [28] C. Yang, H. Feng, and M. Esmaeili-Falak, (2022) “Predicting the compressive strength of modified recycled aggregate concrete" Structural Concrete: DOI: 10.1002/suco.202100681.
  29. [29] R. S. Benemaran and M. Esmaeili-Falak, (2020) “Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO" Computers and Concrete 26(4): 309–316. DOI: 10.12989/cac.2020.26.4.309.
  30. [30] W. Ben Chaabene, M. Flah, and M. L. Nehdi, (2020) “Machine learning prediction of mechanical properties of concrete: Critical review" Construction and Building Materials 260: DOI: 10.1016/j .conbuildmat.2020.119889.
  31. [31] J. Zhang, Y. Huang, F. Aslani, G. Ma, and B. Nener, (2020) “A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete" Journal of Cleaner Production 273: DOI: 10.1016/j.jclepro.2020.122922.
  32. [32] T. Han, A. Siddique, K. Khayat, J. Huang, and A. Kumar, (2020) “An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete" Construction and Building Materials 244: DOI: 10.1016/j.conbuildmat.2020.118271.
  33. [33] Q. Han, C. Gui, J. Xu, and G. Lacidogna, (2019) “A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm" Construction and Building Materials 226: 734–742. DOI: 10.1016/j.conbuildmat.2019.07.315.
  34. [34] A. K. Al-Shamiri, T.-F. Yuan, and J. H. Kim, (2020) “Non-tuned machine learning approach for predicting the compressive strength of high-performance concrete" Materials 13(5): DOI: 10.3390/ma13051023.
  35. [35] D. Fan, R. Yu, Z. Shui, C. Wu, Q. Song, Z. Liu, Y. Sun, X. Gao, and Y. He, (2020) “A new design approach of steel fibre reinforced ultra-high performance concrete composites: Experiments and modeling" Cement and Concrete Composites 110: DOI: 10.1016/j.cemconcomp.2020.103597.
  36. [36] A. Marani and M. L. Nehdi, (2020) “Machine learning prediction of compressive strength for phase change materials integrated cementitious composites" Construction and Building Materials 265: DOI: 10.1016/j.conbuildmat.2020.120286.
  37. [37] A. R. Suleiman and M. L. Nehdi, (2017) “Modeling self-healing of concrete using hybrid genetic algorithmartificial neural network" Materials 10(2): DOI: 10.3390/ma10020135.
  38. [38] O. R. Abuodeh, J. A. Abdalla, and R. A. Hawileh, (2020) “Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques" Applied Soft Computing Journal 95: DOI: 10.1016/j.asoc.2020.106552.
  39. [39] M. Alkaysi and S. El-Tawil, (2017) “Factors affecting bond development between Ultra High Performance Concrete (UHPC) and steel bar reinforcement" Construction and Building Materials 144: 412–422. DOI: 10.1016/j.conbuildmat.2017.03.091.
  40. [40] M. Hassan and K. Wille, (2017) “Experimental impact analysis on ultra-high performance concrete (UHPC) for achieving stress equilibrium (SE) and constant strain rate (CSR) in Split Hopkinson pressure bar (SHPB) using pulse shaping technique" Construction and Building Materials 144: 747–757. DOI: 10.1016/j.conbuildmat.2017.03.185.
  41. [41] H.-O. Jang, H.-S. Lee, K. Cho, and J. Kim, (2017) “Experimental study on shear performance of plain construction joints integrated with ultra-high performance concrete (UHPC)" Construction and Building Materials 152: 16–23. DOI: 10.1016/j.conbuildmat.2017.06.156.
  42. [42] Z. Liu, S. El-Tawil, W. Hansen, and F. Wang, (2018) “Effect of slag cement on the properties of ultra-high performance concrete" Construction and Building Materials 190: 830–837. DOI: 10.1016/j.conbuildmat.2018.09.173.
  43. [43] M. Liew, M. Aswin, K. U. Danyaro, B. S. Mohammed, and A. Al-Yacouby, (2020) “Investigation of fibers reinforced engineered cementitious composites properties using quartz powder" Materials 13(11): DOI: 10.3390/ma13112428.
  44. [44] A. Mohan, S. Karthika, J. Ajith, L. Dhal, and M. Tholkapiyan, (2020) “Investigation on ultra high strength slurry infiltrated multiscale fibre reinforced concrete" Materials Today: Proceedings 22: 904–911. DOI: 10.1016/j.matpr.2019.11.102.
  45. [45] A. H. A. Raheem, M. Mahdy, and A. A. Mashaly, (2019) “Mechanical and fracture mechanics properties of ultra-high-performance concrete" Construction and Building Materials 213: 561–566. DOI: 10.1016/j.conbuildmat.2019.03.298.
  46. [46] G. Gautham Kishore Reddy and P. Ramadoss, (2020) “Influence of alccofine incorporation on the mechanical behavior of ultrahigh performance concrete (UHPC)" Materials Today: Proceedings 33: 789–797. DOI: 10.1016/j.matpr.2020.06.180.
  47. [47] P. Shen, L. Lu, Y. He, F. Wang, and S. Hu, (2019) “The effect of curing regimes on the mechanical properties, nanomechanical properties and microstructure of ultra-high performance concrete" Cement and Concrete Research 118: 1–13. DOI: 10.1016/j.cemconres.2019.01.004.
  48. [48] P. Shen, L. Lu, Y. He, F. Wang, J. Lu, H. Zheng, and S. Hu, (2020) “Investigation on expansion effect of the expansive agents in ultra-high performance concrete" Cement and Concrete Composites 105: DOI: 10.1016/j.cemconcomp.2019.103425.
  49. [49] X. Liang, C. Wu, Y. Su, Z. Chen, and Z. Li, (2018) “Development of ultra-high performance concrete with high fire resistance" Construction and Building Materials 179: 400–412. DOI: 10.1016/j.conbuildmat.2018.05.241.
  50. [50] A. Alsalman, C. N. Dang, G. S. Prinz, and W. M. Hale, (2017) “Evaluation of modulus of elasticity of ultrahigh performance concrete" Construction and Building Materials 153: 918–928. DOI: 10.1016/j.conbuildmat.2017.07.158.
  51. [51] Y. Shi, G. Long, C. Ma, Y. Xie, and J. He, (2019) “Design and preparation of ultra-high performance concrete with low environmental impact" Journal of Cleaner Production 214: 633–643. DOI: 10.1016/j.jclepro.2018.12.318.
  52. [52] M. Shafieifar, M. Farzad, and A. Azizinamini, (2017) “Experimental and numerical study on mechanical properties of Ultra High Performance Concrete (UHPC)" Construction and Building Materials 156: 402–411. DOI: 10.1016/j.conbuildmat.2017.08.170.
  53. [53] M. G. Sohail, B. Wang, A. Jain, R. Kahraman, N. G. Ozerkan, B. Gencturk, M. Dawood, and A. Belarbi, (2018) “Advancements in concrete mix designs: Highperformance and ultrahigh-performance concretes from 1970 to 2016" Journal of Materials in Civil Engineering 30(3): DOI: 10.1061/(ASCE)MT.1943-5533.0002144.
  54. [54] X. Wang, R. Yu, Q. Song, Z. Shui, Z. Liu, S. Wu, and D. Hou, (2019) “Optimized design of ultra-high performance concrete (UHPC) with a high wet packing density" Cement and Concrete Research 126: DOI: 10.1016/j.cemconres.2019.105921.
  55. [55] Z.Wu, K. H. Khayat, and C. Shi, (2019) “Changes in rheology and mechanical properties of ultra-high performance concrete with silica fume content" Cement and Concrete Research 123: DOI: 10.1016/j.cemconres. 2019.105786.
  56. [56] K. Wille and C. Boisvert-Cotulio, (2015) “Material efficiency in the design of ultra-high performance concrete" Construction and Building Materials 86: 33–43. DOI:10.1016/j.conbuildmat.2015.03.087.
  57. [57] X. Chen, D.-w. Wan, L.-z. Jin, K. Qian, and F. Fu, (2019) “Experimental studies and microstructure analysis for ultra high-performance reactive powder concrete" Construction and Building Materials 229: DOI: 10.1016/j.conbuildmat.2019.116924.
  58. [58] M. Chadli, N. Tebbal, and M. Mellas, (2021) “Impact of elevated temperatures on the behavior and microstructure of reactive powder concrete" Construction and Building Materials 300: DOI: 10.1016/j.conbuildmat.2021.124031.
  59. [59] M. Ghrici, S. Kenai, and M. Said-Mansour, (2007) “Mechanical properties and durability of mortar and concrete containing natural pozzolana and limestone blended cements" Cement and Concrete Composites 29(7):542–549. DOI: 10.1016/j.cemconcomp.2007.04.009.
  60. [60] B. A. Graybeal, (2007) “Compressive behavior of ultrahigh-performance fiber-reinforced concrete" ACI Materials Journal 104(2): 146–152.
  61. [61] I. Gamal, K. Elsayed, M. H. Makhlouf, and M. Alaa, (2019) “Properties of reactive powder concrete using local materials and various curing conditions" European Journal of Engineering and Technology Research 4(6): 74–83.
  62. [62] K. Habel, M. Viviani, E. Denarié, and E. Brühwiler, (2006) “Development of the mechanical properties of an Ultra-High Performance Fiber Reinforced Concrete (UHPFRC)" Cement and Concrete Research 36(7): 1362–1370. DOI: 10.1016/j.cemconres.2006.03.009.
  63. [63] A. Hassan, S. Jones, and G. Mahmud, (2012) “Experimental test methods to determine the uniaxial tensile and compressive behaviour of Ultra High Performance Fibre Reinforced Concrete(UHPFRC)" Construction and Building Materials 37: 874–882. DOI: 10.1016/j.conbuildmat.2012.04.030.
  64. [64] M. Ataei, S. Mohammadi, and R. Mikaeil, (2019) “Evaluating performance of cutting machines during sawing dimension stones" Journal of Central South University 26(7): 1934–1945. DOI: 10.1007/s11771-019-4144-1.
  65. [65] J.Wang, Q. Zhou, H. Jiang, and R. Hou, (2015) “Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm" Mathematical Problems in Engineering 2015: DOI: 10.1155/2015/619178.
  66. [66] R. Rajabioun, (2011) “Cuckoo optimization algorithm" Applied Soft Computing Journal 11(8): 5508–5518. DOI: 10.1016/j.asoc.2011.05.008.
  67. [67] B. Roshanravan, H. Aghajani, M. Yousefi, and O. Kreuzer, (2019) “Particle Swarm Optimization Algorithm for Neuro-Fuzzy Prospectivity Analysis Using Continuously Weighted Spatial Exploration Data" Natural Resources Research 28(2): 309–325. DOI: 10.1007/s11053-018-9385-4.
  68. [68] X.-S. Yang, (2010) “A new metaheuristic Bat-inspired Algorithm" Studies in Computational Intelligence 284: 65–74. DOI: 10.1007/978-3-642-12538-6_6.
  69. [69] M.-R. Chen, Y.-Y. Huang, G.-Q. Zeng, K.-D. Lu, and L.-Q. Yang, (2021) “An improved bat algorithm hybridized with extremal optimization and Boltzmann selection" Expert Systems with Applications 175: DOI: 10.1016/j.eswa.2021.114812.
  70. [70] J. Kennedy and R. Eberhart. “Particle swarm optimization”. In: 4. Cited by: 49004. 1995, 1942–1948.
  71. [71] V. Vapnik. The nature of statistical learning theory. Springer science & business media, 1999.
  72. [72] H. Su, X. Li, B. Yang, and Z.Wen, (2018) “Wavelet support vector machine-based prediction model of dam deformation" Mechanical Systems and Signal Processing 110: 412–427. DOI: 10.1016/j.ymssp.2018.03.022.
  73. [73] T. Ayodele, A. Ogunjuyigbe, A. Amedu, and J. Munda, (2019) “Prediction of global solar irradiation using hybridized k-means and support vector regression algorithms" Renewable Energy Focus 29: 78–93. DOI: 10.1016/j.ref.2019.03.003.
  74. [74] F.-K.Wang and T. Mamo, (2018) “A hybrid model based on support vector regression and differential evolution for remaining useful lifetime prediction of lithium-ion batteries" Journal of Power Sources 401: 49–54. DOI: 10.1016/j.jpowsour.2018.08.073.
  75. [75] T. A. Oyehan, I. O. Alade, A. Bagudu, K. O. Sulaiman, S. O. Olatunji, and T. A. Saleh, (2018) “Predicting of the refractive index of haemoglobin using the Hybrid GASVR approach" Computers in Biology and Medicine 98: 85–92. DOI: 10.1016/j.compbiomed.2018.04.024.
  76. [76] W. Yao, C. Zhang, H. Hao, X. Wang, and X. Li, (2018) “A support vectormachine approach to estimate global solar radiation with the influence of fog and haze" Renewable Energy 128: 155–162. DOI: 10.1016/j.renene.2018.05.069.
  77. [77] K. O. Akande, T. O. Owolabi, S. O. Olatunji, and A. AbdulRaheem, (2017) “A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir" Journal of Petroleum Science and Engineering 150: 43–53. DOI: 10.1016/j.petrol.2016.11.033.
  78. [78] M.-W. Li, J. Geng, S. Wang, and W.-C. Hong, (2017) “Hybrid chaotic quantum bat algorithm with SVR in electric load forecasting" Energies 10(12): DOI: 10.3390/en10122180.
  79. [79] T. O. Owolabi, (2019) “Modeling the magnetocaloric effect of manganite using hybrid genetic and support vector regression algorithms" Physics Letters, Section A: General, Atomic and Solid State Physics 383(15): 1782–1790. DOI: 10.1016/j.physleta.2019.02.036.
  80. [80] V. Cherkassky and Y. Ma, (2004) “Practical selection of SVM parameters and noise estimation for SVM regression" Neural Networks 17(1): 113–126. DOI: 10.1016/S0893-6080(03)00169-2.
  81. [81] W.-C. Hong, Y. Dong, W. Y. Zhang, L.-Y. Chen, and B. K. Panigrahi, (2013) “Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm" International Journal of Electrical Power and Energy Systems 44(1): 604–614. DOI: 10.1016/j.ijepes.2012.08.010.


42nd 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.