Computers and Concrete

  • 제28권2호
  • /
  • Pages.221-232
  • /
  • 2021
  • /
  • 1598-8198(pISSN)
  • /
  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Efficient soft computing techniques for the prediction of compressive strength of geopolymer concrete

  • Biswas, Rahul (Department of Civil Engineering, National Institute of Technology) ;
  • Bardhan, Abidhan (Department of Civil Engineering Department, National Institute of Technology Patna) ;
  • Samui, Pijush (Department of Civil Engineering Department, National Institute of Technology Patna) ;
  • Rai, Baboo (Department of Civil Engineering Department, National Institute of Technology Patna) ;
  • Nayak, Subrata (Structural Engineering, ZURU) ;
  • Armaghani, Danial Jahed (Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University)
  • 투고 : 2020.08.21
  • 심사 : 2021.07.13
  • 발행 : 2021.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

In the recent year, extensive researches have been done on fly ash-based geopolymer concrete for its similar properties like Portland cement as well as its environmental sustainability. However, it is difficult to provide a consistent method for geopolymer mix design because of the complexity and uncertainty of its design parameters, such as the alkaline solution concentration, mole ratio, and liquid to fly ash mass ratio. These mix-design parameters, along with the curing time and temperature ominously affect the most significant properties of the geopolymer concrete, i.e., compressive strength. To overcome these difficulties, the paper aims to provide a simple mix-design tool using artificial intelligence (AI) models. Three well-established and efficient AI techniques namely, genetic programming, relevance vector machine, and Gaussian process regression are used. Based on the performance of the developed models, it is understood that all the models have the capability to deliver higher prediction accuracies in the range of 0.9362 to 0.9905 (based on R2 value). Among the employed models, RVM outperformed the other model with R2=0.9905 and RMSE=0.0218. Theodore, the developed RVM model is very potential to be a new alternative to assist engineers to save time and expenditure on account of the trial-and-error process in finding the correct design mix proportions.

키워드

참고문헌

  1. Abdollahzadeh, G., Jahani, E. and Kashir, Z. (2016), "Predicting of compressive strength of recycled aggregate concrete by genetic programming", Comput. Concrete, 18(2). https://doi.org/10.12989/cac.2016.18.2.155.
  2. Adam, A.A. and Horianto. (2014), "The effect of temperature and duration of curing on the strength of fly ash based geopolymer mortar", Procedia Eng., 95, 410-414. https://doi.org/10.1016/j.proeng.2014.12.199.
  3. Al Bakri, A.M., Kamarudin, H., Bnhussain, M., Nizar, I.K., Rafiza, A.R. and Zarina, Y. (2012), "The processing, characterization, and properties of fly ash based geopolymer concrete", Rev. Adv. Mater. Sci., 30, 90-97.
  4. Asikgil, B. and Erar, A. (2000), "Regression error characteristic curves based on the choice of best estimation method", Selcuk J. Appl. Math., 14(2).
  5. Asteris, P.G., Skentou, A.D., Bardhan, A., Samui, P. and Pilakoutas, K. (2021), "Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models", Cement Concrete Res., 145, 106449. https://doi.org/10.1016/j.cemconres.2021.106449.
  6. Bardhan, A., Samui, P., Ghosh, K., Gandomi, A.H. and Bhattacharyya, S. (2021), "ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions", Appl. Sof. Comput., 107595. https://doi.org/10.1016/j.asoc.2021.107595.
  7. Berkelmans, L. and Wang, H. (2012), "Chinese urban residential construction to 2040", Economic Research DepartmentReserve Bank of Australia.
  8. Berry, G. and Armitage, P. (1995), "Mid-P confidence intervals: a brief review", Statistician, 44, 417-423. https://doi.org/10.2307/2348891
  9. Bi, J. and Bennett, K.P. (2003), "Regression error characteristic curves", Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), 43-50.
  10. Biswas, R., Rai, B., Samui, P. and Roy, S.S. (2020), "Estimating concrete compressive strength using MARS, LSSVM and GP", Eng. J., 24(2), 41-52. https://doi.org/10.4186/ej.2020.24.2.41.
  11. Biswas, R., Samui, P. and Rai, B. (2019), "Determination of compressive strength using relevance vector machine and emotional neural network", Asian J. Civil Eng., 20(8), 1109-1118. https://doi.org/10.1007/s42107-019-00171-9.
  12. Chen, C., Habert, G., Bouzidi, Y. and Jullien, A. (2010), "Environmental impact of cement production: detail of the different processes and cement plant variability evaluation", J. Clean. Prod., 18(5), 478-485. https://doi.org/10.1016/j.jclepro.2009.12.014.
  13. Chen, L. and Wang, T.S. (2010), "Modeling strength of high-performance concrete using an improved grammatical evolution combined with macrogenetic algorithm", J. Comput. Civil Eng., 24(3), 281-288. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000031.
  14. Hardjito, D., Wallah, S.E., Sumajouw, D.M. and Rangan, B.V. (2005), "Fly ash-based geopolymer concrete", Austral. J. Struct. Eng., 6(1), 77-86. https://doi.org/10.1080/13287982.2005.11464946.
  15. Davidovits, J. (1991), "Geopolymers-Inorganic polymeric new materials", J. Therm. Anal., 37(8), 1633-1656. https://doi.org/10.1007/BF01912193.
  16. Diaz-Loya, E.I., Allouche, E.N. and Vaidya, S. (2011), "Mechanical properties of fly-ash-based geopolymer concrete", ACI Mater. J., 108(3), 300-306. https://doi.org/620.1u40492-dc22.
  17. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4). https://doi.org/10.12989/cac.2018.21.4.463.
  18. Feng, D., Tan, H. and Van Deventer, J.S.J. (2004), "Ultrasound enhanced geopolymerisation", J. Mater. Sci., 39(2), 571-580. https://doi.org/10.1023/B:JMSC.0000011513.87316.5c.
  19. Fernandez-Jimenez, A.M., Palomo, A. and Lopez-Hombrados, C. (2006), "Engineering properties of alkali-activated fly ash concrete", ACI Mater. J., 103(2), 106-112. https://doi.org/10.1111/j.1745-4530.2008.00353.x.
  20. Fernandez-Jimenez, A., Palomo, A. and Criado, M. (2005), "Microstructure development of alkali-activated fly ash cement: A descriptive model", Cement Concrete Res., 35(6), 1204-1209. https://doi.org/10.1016/j.cemconres.2004.08.021.
  21. Garcia-Lodeiro, I., Palomo, A. and Fernandez-Jimenez, A. (2007), "Alkali-aggregate reaction in activated fly ash systems", Cement Concrete Res., 37(2), 175-183. https://doi.org/10.1016/j.cemconres.2006.11.002.
  22. Ghani, S., Kumari, S. and Bardhan, A. (2021), "A novel liquefaction study for fine-grained soil using PCA-based hybrid soft computing models", Sadhana, 46(3), 113. https://doi.org/10.1007/s12046-021-01640-1.
  23. Golafshani, E.M., Rahai, A. and Kebria, S.S.H. (2014), "Prediction of the bond strength of ribbed steel bars in concrete based on genetic programming", Comput. Concrete, 14(3), 327-345. https://doi.org/10.12989/cac.2014.14.3.327.
  24. Gunasekara, C., Law, D.W., Setunge, S. and Sanjayan, J.G. (2015), "Zeta potential, gel formation and compressive strength of low calcium fly ash geopolymers", Constr. Build. Mater., 95, 592-599. https://doi.org/10.1016/j.conbuildmat.2015.07.175.
  25. Hardjito, D., Wallah, S.E., Sumajouw, D.M.J. and Rangan, B. . (2004), "Factors influencing the compressive strength of fly ash-based geopolymer concrete", Civil Eng. Dimens., 6(2), 88-93. https://doi.org/10.9744/ced.6.2.
  26. Jong, Y.H. and Lee, C.I. (2004), "Influence of geological conditions on the powder factor for tunnel blasting", Int. J. Rock Mech. Min. Sci., 41, 533-538. https://doi.org/10.1016/j.ijrmms.2004.03.095.
  27. Kardani, N., Bardhan, A., Kim, D., Samui, P. and Zhou, A. (2021), "Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO", J. Build. Eng., 35, 102105. https://doi.org/10.1016/j.jobe.2020.102105.
  28. Kardani, N., Bardhan, A., Samui, P., Nazem, M., Zhou, A. and Armaghani, D.J. (2021), "A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil", Eng. Comput., 1-20. https://doi.org/10.1007/s00366-021-01329-3.
  29. Khale, D. and Chaudhary, R. (2007), "Mechanism of geopolymerization and factors influencing its development: A review", J. Mater. Sci., 42(3), 729-746. https://doi.org/10.1007/s10853-006-0401-4.
  30. Koza, J. (1992), Genetic Programming: On The Programming of Computers by Natural Selection, MITPress, Cambridge, MA.
  31. Kumar, M., Bardhan, A., Samui, P., Hu, J.W. and Kaloop, M.R. (2021), "Reliability analysis of pile foundation using soft computing techniques: a comparative study", Process., 9(3), 486. https://doi.org/10.3390/pr9030486.
  32. Ling, Y., Wang, K., Wang, X. and Hua, S. (2019), "Effects of mix design parameters on heat of geopolymerization, set time, and compressive strength of high calcium fly ash geopolymer", Constr. Build. Mater., 228, 116763. https://doi.org/10.1016/j.conbuildmat.2019.116763.
  33. Ling, Y., Wang, K., Wang, X. and Li, W. (2019), "Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks", Neur. Comput. Appl., 3, 85-105. https://doi.org/10.1007/s00521-019-04662-3.
  34. Meyer, C. (2009), "The greening of the concrete industry", Cement Concrete Compos., 8(31), 601-605. https://doi.org/10.1016/j.cemconcomp.2008.12.010.
  35. Nematzadeh, M., Shahmansouri, A.A. and Zabihi, R. (2021), "Innovative models for predicting post-fire bond behavior of steel rebar embedded in steel fiber reinforced rubberized concrete using soft computing methods", Struct., 31, 1141-1162. https://doi.org/10.1016/j.istruc.2021.02.015.
  36. Pacheco-Torgal, F., Castro-Gomes, J. and Jalali, S. (2008), "Alkali-activated binders: A review. Part 1. Historical background, terminology, reaction mechanisms and hydration products", Constr. Build. Mater., 22(7), 1305-1314. https://doi.org/10.1016/j.conbuildmat.2007.10.015.
  37. Provis, J.L. and Bernal, S.A. (2014), "Geopolymers and Related Alkali-Activated Materials", Ann. Rev. Mater. Res., 44(1), 299-327. https://doi.org/10.1146/annurev-matsci-070813-113515.
  38. Rai, B., Roy, L.B. and Rajjak, M. (2018), "A statistical investigation of different parameters influencing compressive strength of fly ash induced geopolymer concrete", Struct. Concrete, 19(5), 1268-1279. https://doi.org/10.1002/suco.201700193.
  39. Rajeshwari, R. and Mandal, S. (2020), "Prediction of fly ash concrete compressive strengths using soft computing techniques", Compu. Concrete, 25(1), 83-94. https://doi.org/10.12989/cac.2020.25.1.083.
  40. Rangan, B. V. (2010), "Fly ash-based geopolymer concrete", Proceedings of the International Workshop on Geopolymer Cement and Concrete, December.
  41. Rattanasak, U. and Chindaprasirt, P. (2009), "Influence of NaOH solution on the synthesis of fly ash geopolymer", Min. Eng., 22(12), 1073-1078. https://doi.org/10.1016/j.mineng.2009.03.022.
  42. Samui, P. (2013), "Determination of compressive strength of concrete by statistical learning algorithms", Eng. J., 17(1), 111-119. https://doi.org/10.4186/ej.2013.17.1.111.
  43. Samui, P. (2015), "Prediction of fracture parameters of concrete by relevance vector machine", Int. J. Eng. Res. Africa, 17, 1-7. https://doi.org/10.4028/www.scientific.net/JERA.17.1.
  44. Samui, P. and Dixon, B. (2012), "Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs", Hydrolog. Proc., 26(9), 1361-1369. https://doi.org/10.1002/hyp.8278.
  45. Sattar, A.M.A., Gharabaghi, B. and McBean, E.A. (2016), "Prediction of timing of watermain failure using gene expression models", Water Resour. Manage., 30(5), 1635-1651. https://doi.org/10.1007/s11269-016-1241-x.
  46. Searson, D.P., Leahy, D.E. and Willis, M.J. (2010), "GPTIPS: An open source genetic programming toolbox for multigene symbolic regression", Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010.
  47. Searson, D., Willis, M. and Montague, G. (2007), "Co-evolution of non-linear PLS model components", J. Chemom., 2, 592-603. https://doi.org/10.1002/cem.1084.
  48. Shaghaghi, S., Bonakdari, H., Gholami, A., Ebtehaj, I. and Zeinolabedini, M. (2017), "Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design", Appl. Math. Comput., 313(C), 271-286. https://doi.org/10.1016/j.amc.2017.06.012.
  49. Shahmansouri, A.A., Bengar, A.H. and AzariJafari, H. (2021), "Life cycle assessment of eco-friendly concrete mixtures incorporating natural zeolite in sulfate-aggressive environment", Constr. Build. Mater., 268, 121136. https://doi.org/10.1016/j.conbuildmat.2020.121136.
  50. Shahmansouri, A.A., Bengar, A.. and Ghanbari, S. (2020), "Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method", J. Build. Eng., 31, 101326. https://doi.org/10.1016/j.jobe.2020.101326.
  51. Shahmansouri, A.A., Nematzadeh, M. and Behnood, A. (2021), "Mechanical properties of GGBFS-based geopolymer concrete incorporating natural zeolite and silica fume with an optimum design using response surface method", J. Build. Eng., 36, 102138. https://doi.org/10.1016/j.jobe.2020.102138.
  52. Shahmansouri, A.A., Yazdani, M., Ghanbari, S., Akbarzadeh Bengar, H., Jafari, A. and Farrokh Ghatte, H. (2021), "Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite", J. Clean. Prod., 279, 123697. https://doi.org/10.1016/j.jclepro.2020.123697.
  53. Silva, P., De Sagoe-Crenstil, K. and Sirivivatnanon, V. (2007), "Kinetics of geopolymerization: Role of Al2O3 and SiO2", Cement Concrete Res., 37(4), 512-518. https://doi.org/10.1016/j.cemconres.2007.01.003.
  54. Skvara, F., Svoboda, P., Dolezal, J., Kopecky, L., Pawlasova, S., Myskova, L., Lucuk, M., Dvoracek, K., Beksa, M. and Sulc, R. (2006), "Concrete based on fly ash geopolymer", Materials, Experimentation, Maintenance and Rehabilitation - Proceedings of the 10th East Asia-Pacific Conference on Structural Engineering and Construction, EASEC 2010.
  55. Song, X. (2007), "Development and performance of Class F fly ash based geopolymer concretes against sulphuric acid attack", The University of New South Wales.
  56. Songpiriyakij, S., Kubprasit, T., Jaturapitakkul, C. and Chindaprasirt, P. (2010), "Compressive strength and degree of reaction of biomass- and fly ash-based geopolymer", Constr. Build. Mater., 24(3), 236-240. https://doi.org/10.1016/j.conbuildmat.2009.09.002.
  57. Taylor, K.E. (2001), "Summarizing multiple aspects of model performance in a single diagram", J. Geophys. Res. Atmosphere., 106(7), 7183-7192. https://doi.org/10.1029/2000JD900719.
  58. Tennakoon, C., Nazari, A., Sanjayan, J.G. and Sagoe-Crentsil, K. (2014), "Distribution of oxides in fly ash controls strength evolution of geopolymers", Constr. Build. Mater., 74, 72-82. https://doi.org/10.1016/j.conbuildmat.2014.08.016.
  59. Tipping, M. (2001), "Sparse Bayesian learning and the relevance vector mach", J. Mach. Learn. Res., 1, 211-244. https://doi.org/10.1162/15324430152748236.
  60. Zuda, L., Pavlik, Z., Rovnanikova, P., Bayer, P. and Cerny, R. (2006), "Properties of alkali activated aluminosilicate material after thermal load", Int. J. Thermophys., 27(4), 1250-1263. https://doi.org/10.1007/s10765-006-0077-7.