Advertisement

Neural Computing and Applications

, Volume 31, Supplement 1, pp 409–424 | Cite as

Self-compacting concrete strength prediction using surrogate models

  • Panagiotis G. AsterisEmail author
  • Konstantinos G. Kolovos
Original Article

Abstract

Despite the extensive use of self-compacting concrete in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength based on its mix components. Τhis limitation is due to the highly nonlinear relation between the self-compacting concrete’s compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the mechanical characteristics of self-compacting concrete has been investigated. Specifically, surrogate models (such as artificial neural network models and a new proposed normalization method) have been used for predicting the 28-day compressive strength of admixture-based self-compacting concrete (based on experimental data available in the literature). The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of self-compacting concrete in a reliable and robust manner. Furthermore, the proposed formula for the normalization of data has been proven effective and robust compared to available ones.

Keywords

Artificial neural networks Back propagation neural networks Compressive strength Self-compacting concrete 

Notes

Acknowledgments

The research was performed within the framework of the Master’s Program in Applied Computational Structural Engineering (ACSE), which has been partially financed by the Research Committee of the School of Pedagogical and Technological Education, Athens, Greece. The authors would like to express their gratitude to MSc students Mrs. M.G. Douvika, Mr. K. Roinos and Mr. A.K. Tsaris and to the undergraduate students Mr. N. Margaris and Mr. D. Georgakopoulos for their assistance on the computational implementation of the ANN models.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Açikgenç M, Ulaş M, Alyamaç KE (2015) Using an artificial neural network to predict mix compositions of steel fiber-reinforced concrete. Arab J Sci Eng 40(2):407–419CrossRefGoogle Scholar
  2. 2.
    Adeli H (2001) Neural networks in civil engineering: 1989-2000. Computer-aided civil and infrastructure engineering 16(2):126–142CrossRefGoogle Scholar
  3. 3.
    Akkurt S, Tayfur G, Can S (2004) Fuzzy logic model for the prediction of cement compressive strength. Cem Concr Res 34(8):1429–1433CrossRefGoogle Scholar
  4. 4.
    Alyamac KE, Ince R (2009) A preliminary concrete mix design for SCC with marble powders. Constr Build Mater 23:1201–1210CrossRefGoogle Scholar
  5. 5.
    Asteris, P.G., Plevris, V. (2013). Neural network approximation of the masonry failure under biaxial compressive stress, ECCOMAS Special Interest Conference—SEECCM 2013: 3rd South-East European Conference on Computational Mechanics, Proceedings—an IACM Special Interest Conference, pp. 584–598Google Scholar
  6. 6.
    Asteris PG, Plevris V (2016) Anisotropic masonry failure criterion using artificial neural networks. Neural Computing and Applications (NCAA). doi: 10.1007/s00521-016-2181-3 Google Scholar
  7. 7.
    Asteris PG, Tsaris AK, Cavaleri L, Repapis CC, Papalou A, Di Trapani F, Karypidis DF (2016a) Prediction of the fundamental period of infilled RC frame structures using artificial neural networks. Computational Intelligence and Neuroscience 2016:5104907CrossRefGoogle Scholar
  8. 8.
    Asteris PG, Kolovos KG, Douvika MG, Roinos K (2016b) Prediction of self-compacting concrete strength using artificial neural networks. European Journal of Environmental and Civil Engineering 20:s102–s122CrossRefGoogle Scholar
  9. 9.
    Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans Inf Theory 44(2):525–536MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Baskar I, Ramanathan P, Venkatasubramani R (2012) Influence of silica fume on properties of self-compacting concrete. Int J Emerg Trends Eng Dev 4:757–767Google Scholar
  11. 11.
    Baykal G, Döven AG (2000) Utilization of fly ash as pelletization process; theory, application, areas and research results. Resour Conserv Recycl 30:59–77CrossRefGoogle Scholar
  12. 12.
    Baykasoǧlu A, Dereli TU, Taniş S (2004) Prediction of cement strength using soft computing techniques. Cem Concr Res 34(11):2083–2090CrossRefGoogle Scholar
  13. 13.
    Belalia Douma O, Boukhatem B, Ghrici M, Tagnit-Hamou A (2016) Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Comput & Applic. doi: 10.1007/s00521-016-2368-7 Google Scholar
  14. 14.
    Berry MJA, Linoff G (1997) Data mining techniques. Wiley, NYGoogle Scholar
  15. 15.
    Blum A (1992) Neural networks in C++. Wiley, NYGoogle Scholar
  16. 16.
    Boger, Z, Guterman, H (1997) Knowledge extraction from artificial neural network models, IEEE Systems, Man, and Cybernetics Conference, Orlando, FL, USAGoogle Scholar
  17. 17.
    Boukendakdji O, Kenai S, Kadri EH, Rouis F (2009) Effect of slag on the rheology of fresh self-compacted concrete. Constr Build Mater 23:2593–2598CrossRefGoogle Scholar
  18. 18.
    Boukendakdji O, Kadri EH, Kenai S (2012) Effects of granulated blast furnace slag and superplasticizer type on the fresh properties and compressive strength of self-compacting concrete. Constr Build Mater 34:583–590Google Scholar
  19. 19.
    Brouwers HJH, Radix HJ (2005) Self-compacting concrete: theoretical and experimental study. Cem Concr Res 35:2116–2136CrossRefGoogle Scholar
  20. 20.
    Chen Z (2013) An overview of bayesian methods for neural spike train analysis. Computational Intelligence and Neuroscience 2013:Article number 251905CrossRefGoogle Scholar
  21. 21.
    Delen D, Sharda R, Bessonov M (2006) Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accid Anal Prev 38(3):434–444CrossRefGoogle Scholar
  22. 22.
    Dias WPS, Pooliyadda SP (2001) Neural networks for predicting properties of concretes with admixtures. Constr Build Mater 15(7):371–379CrossRefGoogle Scholar
  23. 23.
    Dinakar P, Sethy KP, Sahoo UC (2013) Design of self-compacting concrete with ground granulated blast furnace slag. Mater Des 43:161–169CrossRefGoogle Scholar
  24. 24.
    Fathi A, Shafiq N, Nuruddin MF, Elheber A (2013) Study the effectiveness of the different pozzolanic material on self-compacting concrete. ARPN J Eng Applied Sci 8:229–305Google Scholar
  25. 25.
    Felekoglu B, Turkel S, Baradan B (2007) Effect of water/cement ratio on the fresh and hardened properties of self-compacting concrete. Build Environ 42:1795–1802CrossRefGoogle Scholar
  26. 26.
    Gandage, AS, Ram, VV, Sivakumar, MVN, Vasan, A, Venu, M, Yaswanth, AB (2013) Optimization of class C flyash dosage in self-compacting concrete for pavement applications, Proceedings of the International Conference on Innovations in Concrete for Meeting Infrastructure Challenge, October 23–26, 2013, Hyderabad, Andhra Pradesh, India, pp: 213–226Google Scholar
  27. 27.
    Gesoglu M, Ozbay E (2007) Effects of mineral admixtures on fresh and hardened properties of self-compacting concretes: binary, ternary and quaternary systems. Mater Struct 40:923–937CrossRefGoogle Scholar
  28. 28.
    Gesoglu M, Guneyisi E, Ozbay E (2009) Properties of self-compacting concretes made with binary, ternary and quarternary cementitious blends of fly ash, blast furnace slag and silica fume. Constr Build Mater 23:1847–1854CrossRefGoogle Scholar
  29. 29.
    Gettu, R., Izquierdo, J., Gomes, P.C.C., Josa, A. (2002). Development of high-strength self-compacting concrete with fly ash: a four-step experimental methodology, Proceedings of the 27th Conference on Our World in Concrete and Structures, August 29–30, 2002, Singapore, pp: 217–224Google Scholar
  30. 30.
    Giovanis DG, Papadopoulos V (2015) Spectral representation-based neural network assisted stochastic structural mechanics. Engineering Structures, Volume 84:382–394CrossRefGoogle Scholar
  31. 31.
    Grdic Z, Despotovic I, Curcic GT (2008) Properties of self-compacting concrete with different types of additives. Facta Universitatis-Ser: Archit Civil Eng 6:173–177Google Scholar
  32. 32.
    Güneyisi E, Gesoglu M, Ali Azez O, Öznur Öz H (2016) Effect of nano silica on the workability of self-compacting concretes having untreated and surface treated lightweight aggregates. Constr Build Mater 115:371–380CrossRefGoogle Scholar
  33. 33.
    Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366CrossRefzbMATHGoogle Scholar
  34. 34.
    Iruansi, O, Guadagnini, M, Pilakoutas, K, Neocleous, K (2010) Predicting the shear strength of RC beams without stirrups using Bayesian neural network, in 4th International Workshop on Reliable Engineering Computing (REC 2010)Google Scholar
  35. 35.
    Joseph G, Ramamurthy K (2009) Influence of fly ash on strength and sorption characteristics of cold-bonded fly ash aggregate concrete. Constr Build Mater 23:1862–1870CrossRefGoogle Scholar
  36. 36.
    Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence And Expert Systems (IJAE) 1(4):111–122Google Scholar
  37. 37.
    Kayali O (2008) Fly ash lightweight aggregates in high performance concrete. Constr Build Mater 22:2393–2399CrossRefGoogle Scholar
  38. 38.
    Lamanna J, Malgaroli A, Cerutti S, Signorini MG (2012) Detection of fractal behavior in temporal series of synaptic quantal release events: a feasibility study, Computational Intelligence and Neuroscience, volume 2012, 2012. Article number 704673Google Scholar
  39. 39.
    Lee SC (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25(7):849–857CrossRefGoogle Scholar
  40. 40.
    Lourakis MIA (2005). A brief description of the Levenberg-Marquardt algorithm implemened by levmar. Institute of Computer Science Foundation for Research and Technology - Hellas (FORTH), available at: http://www.ics.forth.gr/~lourakis/levmar/levmar.pdf.
  41. 41.
    Malagavelli V, Manalel PA (2014) Modeling of compressive strength of admixture-based self compacting concrete using fuzzy logic and artificial neural networks. Asian Journal of Applied Sciences 7(7):536–551CrossRefGoogle Scholar
  42. 42.
    Mansouri I, Kisi O (2015) Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches. Compos Part B 70:247–255CrossRefGoogle Scholar
  43. 43.
    Mansouri, I., Gholampour, A., Kisi, O., Ozbakkaloglu, T. (2016). Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques, neural computing and applications, pp. 1-16Google Scholar
  44. 44.
    Mashhadban H, Kutanaei SS, Sayarinejad MA (2016) Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network. Constr Build Mater 119:277–287CrossRefGoogle Scholar
  45. 45.
    Memon SA, Shaikh MA, Akbar H (2011) Utilization of rice husk ash as viscosity modifying agent in self compacting concrete. Constr Build Mater 25:1044–1048CrossRefGoogle Scholar
  46. 46.
    Özcan F, Atiş CD, Karahan O, Uncuoǧlu E, Tanyildizi H (2009) Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Adv Eng Softw 40(9):856–863CrossRefzbMATHGoogle Scholar
  47. 47.
    Papadopoulos V, Giovanis DG, Lagaros ND, Papadrakakis M (2012) Accelerated subset simulation with neural networks for reliability analysis. Comput Methods Appl Mech Eng 223-224:70–80MathSciNetCrossRefzbMATHGoogle Scholar
  48. 48.
    Pattnaik S, Karunakar DB, Jha PK (2014) A prediction model for the lost wax process through fuzzy-based artificial neural network, Proceedings of the Institution of Mechanical Engineers. Part C: Journal of Mechanical Engineering Science 228(7):1259–1271Google Scholar
  49. 49.
    Phadke MS (1989) Quality engineering using design of experiments. In Quality control, robust design, and the Taguchi method. Springer, US, pp 31–50CrossRefGoogle Scholar
  50. 50.
    Phani SS, Sekhar ST, Rao S, Sravana P (2013) High strength self-compacting concrete using mineral admixtures. Indian Concr J 87:42–47Google Scholar
  51. 51.
    Plevris, V, Asteris, PG (2014a) Modeling of masonry compressive failure using Neural Networks, OPT-i 2014—1st International Conference on Engineering and Applied Sciences Optimization, Proceedings, pp. 2843–2861Google Scholar
  52. 52.
    Plevris V, Asteris PG (2014b) Modeling of masonry failure surface under biaxial compressive stress using neural networks. Constr Build Mater 55:447–461CrossRefGoogle Scholar
  53. 53.
    Plevris, V, Asteris, P (2015) Anisotropic failure criterion for brittle materials using Artificial Neural Networks, COMPDYN 2015—5th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, pp. 2259–2272Google Scholar
  54. 54.
    Rahman ME, Muntohar AS, Pakrashi V, Nagaratnam BH, Sujan D (2014) Self-compacting concrete from uncontrolled burning of rice husk and blended fine aggregate. Mater Des 55:410–415CrossRefGoogle Scholar
  55. 55.
    Rao, NVR, Rao, PS, Sravana, P, Sekhar, TS (2009). Studies on relationship of water-powder ratio and compressive strength of self-compacted concrete, Proceedings of the 34th Conference on Our World in Concrete and Structures, August 16–18, 2009, Singapore, pp: 1–8Google Scholar
  56. 56.
    Rouis F (2009) Effect of slag on the rheology of fresh self-compacted concrete. Constr Build Mater 23:2593–2598CrossRefGoogle Scholar
  57. 57.
    Safiuddin M, Raman SN, Salam MA, Jumaat MZ (2016) Modeling of compressive strength for self-consolidating high-strength concrete incorporating palm oil fuel ash. Materials 9(5):396CrossRefGoogle Scholar
  58. 58.
    Sahmaran M, Yaman IO, Tokyay M (2009) Transport and mechanical properties of self-consolidating concrete with high volume fly ash. Cem Concr Compos 31:99–106CrossRefGoogle Scholar
  59. 59.
    Sfikas IP, Trezos KG (2013) Effect of composition variations on bond properties of self-compacting concrete specimens. Constr Build Mater 41:252–262CrossRefGoogle Scholar
  60. 60.
    Siddique R (2011) Properties of self-compacting concrete containing class F fly ash. Mater Des 32:1501–1507CrossRefGoogle Scholar
  61. 61.
    Sonebi M (2004) Medium strength self-compacting concrete containing fly ash: modelling using factorial experimental plans. Cem Concr Res 34:1199–1208CrossRefGoogle Scholar
  62. 62.
    Sukumar B, Nagamani K, Raghavan RS (2008) Evaluation of strength at early ages of self-compacting concrete with high volume fly ash. Constr Build Mater 22:1394–1401CrossRefGoogle Scholar
  63. 63.
    Topçu IB, Saridemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 41(3):305–311CrossRefGoogle Scholar
  64. 64.
    Trtnik G, Kavčič F, Turk G (2009) Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics 49(1):53–60CrossRefGoogle Scholar
  65. 65.
    Valcuende M, Marco E, Parra C, Serna P (2012) Influence of limestone filler and viscosity-modifying admixture on the shrinkage of self-compacting concrete. Cem Concr Res 42:583–592CrossRefGoogle Scholar
  66. 66.
    Waszczyszyn Z, Ziemiański L (2001) Neural networks in mechanics of structures and materials—new results and prospects of applications. Comput Struct 79(22–25):2261–2276CrossRefGoogle Scholar
  67. 67.
    Zhao H, Sun W, Wu X, Gao B (2015) The properties of the self-compacting concrete with fly ash and ground granulated blast furnace slag mineral admixtures. J Clean Prod 95:66–74CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Panagiotis G. Asteris
    • 1
    Email author
  • Konstantinos G. Kolovos
    • 2
  1. 1.Computational Mechanics LaboratorySchool of Pedagogical and Technological EducationHeraklionGreece
  2. 2.Department of Physical Sciences and ApplicationsHellenic Army AcademyVariGreece

Personalised recommendations