Steel and Composite Structures

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Application of a comparative analysis of random forest programming to predict the strength of environmentally-friendly geopolymer concrete

  • Ying Bi (School of Civil Engineering and Architecture, Zhengzhou Shengda University of Economics, Business & Management) ;
  • Yeng Yi (Department of Civil Engineering, Huazhong University)
  • Received : 2023.08.26
  • Accepted : 2023.01.24
  • Published : 2024.02.25
⟨ Previous Next ⟩

Abstract

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO2 emissions in the building industry. In the present work, the compressive strength (fc) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R2 metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

Keywords

Acknowledgement

This work was supported by Henan Provincial Department of Science and Technology Science and Technology Research Program Project, 212102310284, Research on seismic performance of prefabricated shear walls connected to vertical steel structures.

References

  1. Aghapour, A.H., Yazdani, M., Jolai, F. and Mojtahedi, M. (2019), "Capacity planning and reconfiguration for disaster-resilient health infrastructure", J. Build. Eng., 26, 100853. https://doi.org/10.1016/j.jobe.2019.100853.
  2. Ahmadi, B. and Shekarchi, M. (2010), "Use of natural zeolite as a supplementary cementitious material", Cement Concrete Compos., 32(2), 134-141. https://doi.org/10.1016/j.cemconcomp.2009.10.006
  3. Ahmed, H.U., Mohammed, A.A. and Mohammed, A. (2022), "Soft computing models to predict the compressive strength of GGBS/FA-geopolymer concrete", PloS One, 17(5), e0265846. https://doi.org/10.1371/journal.pone.0265846.
  4. Aneja, S., Sharma, A., Gupta, R. and Yoo, D.-Y. (2021), "Bayesian regularized artificial neural network model to predict strength characteristics of fly-ash and bottom-ash based geopolymer concrete", Materials, 14(7), 1729. https://doi.org/10.3390/ma14071729.
  5. Ashrafian, A., Amiri, M.J.T., Rezaie-Balf, M., Ozbakkaloglu, T. and Lotfi-Omran, O. (2018), "Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods", Construct. Build. Mater., 190, 479-494. https://doi.org/10.1016/j.conbuildmat.2018.09.047.
  6. AzariJafari, H., Amiri, M.J.T., Ashrafian, A., Rasekh, H., Barforooshi, M.J. and Berenjian, J. (2019), "Ternary blended cement: An eco-friendly alternative to improve resistivity of high-performance self-consolidating concrete against elevated temperature", J. Cleaner Product., 223, 575-586. https://doi.org/10.1016/j.jclepro.2019.03.054.
  7. Babagolzadeh, M., Shrestha, A., Abbasi, B., Zhang, Y., Woodhead, A. and Zhang, A. (2020), "Sustainable cold supply chain management under demand uncertainty and carbon tax regulation", Transportat. Res. Part D: Transport Environ., 80, 102245. https://doi.org/10.1016/j.trd.2020.102245.
  8. Bengar, H.A., Shahmansouri, A.A., Sabet, N.A.Z., Kabirifar, K. and Tam, V.W.Y. (2020), "Impact of elevated temperatures on the structural performance of recycled rubber concrete: Experimental and mathematical modeling", Construct. Build. Mater., 255, 119374. https://doi.org/10.1016/j.conbuildmat.2020.119374.
  9. Blash, A.M.A. and Lakshmi, T.V.S.V. (2016), "Properties of geopolymer concrete produced by silica fume and ground-granulated blast-furnace slag", Int. J. Sci. Res, 5(10), 319-323.
  10. Celikten, S., Saridemir, M. and Deneme, I.O. (2019), "Mechanical and microstructural properties of alkali-activated slag and slag+ fly ash mortars exposed to high temperature", Construct. Build. Mater., 217, 50-61. https://doi.org/10.1016/j.conbuildmat.2019.05.055.
  11. Collins, F. and Sanjayan, J. G. (1999), "Effects of ultra-fine materials on workability and strength of concrete containing alkali-activated slag as the binder", Cement Concrete Res., 29(3), 459-462. https://doi.org/10.1016/S0008-8846(98)00237-3.
  12. Degirmenci, F.N. (2017), Effect of Sodium Silicate to Sodium Hydroxide Ratios on Durability of Geopolymer Mortars Containing Natural and Artificial Pozzolans. https://doi.org/10.13168/cs.2017.0033.
  13. Degirmenci, F.N. (2018), "Utilization of natural and waste pozzolans as an alternative resource of geopolymer mortar", Int. J. Civil Eng., 16(2), 179-188. https://doi.org/10.1007/s40999-016-0115-1.
  14. Divvala, S. (2021), "Early strength properties of geopolymer concrete composites: an experimental study", Mater. Today: Proceedings, 47, 3770-3777. https://doi.org/10.1016/j.matpr.2021.03.002.
  15. Dutta, D., Thokchom, S., Ghosh, P. and Ghosh, S. (2010), "Effect of silica fume additions on porosity of fly ash geopolymers", J. Eng. Appl. Sci, 5(10), 74-79.
  16. Duxson, P., Fernandez-Jimenez, A., Provis, J.L., Lukey, G.C., Palomo, A. and van Deventer, J.S.J. (2007), "Geopolymer technology: the current state of the art", J. Mater. Sci., 42(9), 2917-2933. https://doi.org/10.1007/s10853-006-0637-z.
  17. Erfanimanesh, A. and Sharbatdar, M.K. (2020), "Mechanical and microstructural characteristics of geopolymer paste, mortar, and concrete containing local zeolite and slag activated by sodium carbonate", J. Build. Eng., 32, 101781. https://doi.org/10.1016/j.jobe.2020.101781.
  18. Farrar, D.E. and Glauber, R.R. (1967), "Multicollinearity in regression analysis: the problem revisited", Rev. Economic Statistics, 92-107. https://doi.org/10.2307/1937887.
  19. Fathollahi-Fard, A.M., Govindan, K., Hajiaghaei-Keshteli, M. and Ahmadi, A. (2019), "A green home health care supply chain: New modified simulated annealing algorithms", J. Cleaner Product., 240, 118200. https://doi.org/10.1016/j.jclepro.2019.118200.
  20. Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M. and Tavakkoli-Moghaddam, R. (2018), "A bi-objective green home health care routing problem", J. Cleaner Product., 200, 423-443. https://doi.org/10.1016/j.jclepro.2018.07.258.
  21. Ghasemi, M., Rasekh, H., Berenjian, J. and AzariJafari, H. (2019), "Dealing with workability loss challenge in SCC mixtures incorporating natural pozzolans: A study of natural zeolite and pumice", Construct. Build. Mater., 222, 424-436. https://doi.org/10.1016/j.conbuildmat.2019.06.174.
  22. Gholhaki, M., Hajforoush, M. and Kazemi, M. (2018), "An investigation on the fresh and hardened properties of self-compacting concrete incorporating magnetic water with various pozzolanic materials", Construct. Build. Mater., 158, 173-180. https://doi.org/10.1016/j.conbuildmat.2017.09.135.
  23. Gupta, A. (2021), "Investigation of the strength of ground granulated blast furnace slag based geopolymer composite with silica fume", Mater. Today: Proceedings, 44, 23-28. https://doi.org/10.1016/j.matpr.2020.06.010.
  24. Gupta, P., Gupta, N., Saxena, K.K. and Goyal, S. (2021), "Multilayer perceptron modelling of geopolymer composite incorporating fly ash and GGBS for prediction of compressive strength", Adv. Mater. Processing Technol., 1-15. https://doi.org/10.1080/2374068X.2021.1946751.
  25. Hajforoush, M., Madandoust, R. and Kazemi, M. (2019), "Effects of simultaneous utilization of natural zeolite and magnetic water on engineering properties of self-compacting concrete", Asian J. Civil Eng., 20(2), 289-300. https://doi.org/10.1007/s42107-018-00106-w.
  26. Hayyolalam, V. and Kazem, A.A.P. (2020), "Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems", Eng. Appl. Artificial Intel., 87, 103249. https://doi.org/10.1016/j.engappai.2019.10324.
  27. Hurlimann, A.C., Warren-Myers, G. and Browne, G.R. (2019), "Is the Australian construction industry prepared for climate change", Build. Environ., 153, 128-137. https://doi.org/10.1016/j.buildenv.2019.02.008.
  28. Karthik, S. and Mohan, K.S.R. (2021), "A taguchi approach for optimizing design mixture of geopolymer concrete incorporating fly ash, ground granulated blast furnace slag and silica fume", Crystals, 11(11), 1279. https://doi.org/10.3390/cryst11111279.
  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. Khan, M.A., Zafar, A., Farooq, F., Javed, M.F., Alyousef, R., Alabduljabbar, H. and Khan, M.I. (2021), "Geopolymer concrete compressive strength via artificial neural network, adaptive neuro fuzzy interface system, and gene expression programming with K-fold cross validation", Front. Mater., 8, 621163. https://doi.org/10.3389/fmats.2021.621163.
  31. Khishe, M. and Mosavi, M.R. (2020), "Chimp optimization algorithm", Expert Syst. Appl., 149, 113338. https://doi.org/10.1016/j.eswa.2020.113338.
  32. Kumar, Y.N., Kumar, B.D. and Swami, B.L.P. (n.d.), Mechanical Properties of Geopolymer Concrete with Combined Mineral Admixtures.
  33. Li, C., Sun, H. and Li, L. (2010), "A review: The comparison between alkali-activated slag (Si+ Ca) and metakaolin (Si+ Al) cements", Cement Concrete Res., 40(9), 1341-1349. https://doi.org/10.1016/j.cemconres.2010.03.020.
  34. Li, Y.W., Li, G.Q., Xiao, L., Yam, M.C. and Zhang, J.Z. (2023), "Shear resistance of corrugated web steel beams with circular web openings: Test and machine learning-based prediction", Steel Compos. Struct., 47(1), 103-117. https://doi.org/10.12989/scs.2023.47.1.103.
  35. Lin, L., Xu, J., Yuan, J. and Yu, Y. (2023), "Compressive strength and elastic modulus of RBAC: An analysis of existing data and an Artificial Intelligence Based Prediction", Case Studies Construct. Mater., e02184. https://doi.org/10.1016/j.cscm.2023.e02184.
  36. Mahmoudi, P., Mohammadi, M. and Daneshmand, H. (2019), "Investigating the trend of average changes of annual temperatures in Iran", Int. J. Environ. Sci. Technol., 16(2), 1079-1092. https://doi.org/10.1007/s13762-018-1664-4.
  37. Meng, Q., Wu, C., Su, Y., Li, J., Liu, J. and Pang, J. (2019), "Experimental and numerical investigation of blast resistant capacity of high performance geopolymer concrete panels", Compos. Part B: Eng., 171, 9-19. https://doi.org/10.1016/j.compositesb.2019.04.010.
  38. Naderpour, H., Nagai, K., Fakharian, P. and Haji, M. (2019), "Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods", Compos. Struct., 215, 69-84. https://doi.org/10.1016/j.compstruct.2019.02.048.
  39. Nhu, V.-H., Hoang, N.-D., Duong, V.-B., Vu, H.-D. and Bui, D. T. (2020), "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., 36(2), 603-616. https://doi.org/10.1007/s00366-019-00718-z.
  40. Okoye, F.N., Prakash, S. and Singh, N.B. (2017), "Durability of fly ash based geopolymer concrete in the presence of silica fume", J. Cleaner Product., 149, 1062-1067. https://doi.org/10.1016/j.jclepro.2017.02.176.
  41. Ozaydin, S., Kocer, G. and Hepbasli, A. (2006), "Natural zeolites in energy applications", Energy Sources, Part A, 28(15), 1425-1431. https://doi.org/10.1080/15567240500400804.
  42. Pacheco-Torgal, F., Abdollahnejad, Z., Camoes, A.F., Jamshidi, M. and Ding, Y. (2012), "Durability of alkali-activated binders: a clear advantage over Portland cement or an unproven issue?", Construct. Build. Mater., 30, 400-405. https://doi.org/10.1016/j.conbuildmat.2011.12.017.
  43. Padmakar, M., Barhmaiah, B. and Priyanka, M.L. (2021), "Characteristic compressive strength of a geo polymer concrete", Mater. Today: Proceedings, 37, 2219-2222. https://doi.org/10.1016/j.matpr.2020.07.656.
  44. Pham, V.T. and Kim, S.E. (2023), "A robust approach in prediction of RCFST columns using machine learning algorithm", Steel Compos. Struct., 46(2), 153-173. https://doi.org/10.12989/scs.2023.46.2.153.
  45. Pham, V.T., Son, H.S., Kim, C.H., Jang, Y. and Kim, S.E. (2023), "A novel method for vehicle load detection in cable-stayed bridge using graph neural network", Steel Compos. Struct., 46(6), 731. https://doi.org/10.12989/scs.2023.46.6.731.
  46. Premkumar, R., Hariharan, P. and Rajesh, S. (2022), "Effect of silica fume and recycled concrete aggregate on the mechanical properties of GGBS based geopolymer concrete", Mater. Today: Proceedings, 60, 211-215. https://doi.org/10.1016/j.matpr.2021.12.442
  47. Qiu, J., Dai, N. and Alavijeh, A.S. (2023), "Application of computer algorithms for modelling and numerical solution of dynamic bending", Steel Compos. Struct., 46(1), 143. https://doi.org/10.12989/scs.2023.46.1.143.
  48. Rao, A.K. and Kumar, D.R. (2020), "Comparative study on the behaviour of GPC using silica fume and fly ash with GGBS exposed to elevated temperature and ambient curing conditions", Mater. Today: Proceedings, 27, 1833-1837. https://doi.org/10.1088/1757-899X/998/1/012060.
  49. Rao, G.M., Kumar, K.S., Poloju, K.K., and Srinivasu, K. (2020), "An emphasis of geopolymer concrete with single activator and conventional concrete with recycled aggregate and data analyzing using artificial neural network", IOP Conference Series: Materials Science and Engineering, 998(1), 12060. https://doi.org/10.1016/j.matpr.2020.03.789.
  50. Rashad, A.M. (2014), "A comprehensive overview about the influence of different admixtures and additives on the properties of alkali-activated fly ash", Mater. Des., 53, 1005-1025. https://doi.org/10.1016/j.matdes.2013.07.07.
  51. Saini, G. and Vattipalli, U. (2020), "Assessing properties of alkali activated GGBS based self-compacting geopolymer concrete using nano-silica", Case Studies Construct. Mater., 12, e00352. https://doi.org/10.1016/j.cscm.2020.e00352.
  52. Shahmansouri, A.A., Bengar, H.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.
  53. Shahmansouri, A.A., Bengar, H.A. and Jahani, E. (2019), "Predicting compressive strength and electrical resistivity of eco-friendly concrete containing natural zeolite via GEP algorithm", Construct. Build. Mater., 229, 116883. https://doi.org/10.1016/j.conbuildmat.2019.116883.
  54. 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.
  55. Shahmansouri, A.A., Yazdani, M., Ghanbari, S., Bengar, H.A., Jafari, A. and Ghatte, H. F. (2021), "Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite", J. Cleaner Product., 279, 123697. https://doi.org/10.1016/j.jclepro.2020.123697.
  56. Supraja, V. and Rao, M.K. (2012), "Experimental study on Geo-Polymer concrete incorporating GGBS", Int. J. Electronics, Commun. Soft Comput. Sci. Eng. (IJECSCSE), 2(2), 11.
  57. Tekin, I., Gencel, O., Gholampour, A., Oren, O.H., Koksal, F. and Ozbakkaloglu, T. (2020), "Recycling zeolitic tuff and marble waste in the production of eco-friendly geopolymer concretes", J. Cleaner Product., 268, 122298. https://doi.org/10.1016/j.jclepro.2020.122298.
  58. Toghroli, A., Mehrabi, P., Shariati, M., Trung, N.T., Jahandari, S. and Rasekh, H. (2020), "Evaluating the use of recycled concrete aggregate and pozzolanic additives in fiber-reinforced pervious concrete with industrial and recycled fibers", Construct. Build. Mater., 252, 118997. https://doi.org/10.1016/j.conbuildmat.2020.118997.
  59. Wang, S., Xu, J., Wang, Y. and Pan, C. (2023), "Machine learning-based prediction of shear strength of steel reinforced concrete columns subjected to axial compressive load and seismic lateral load", Structures, 56, 104968. https://doi.org/10.1016/j.istruc.2023.104968.
  60. Xu, J., Chen, Y., Xie, T., Zhao, X., Xiong, B. and Chen, Z. (2019a), "Prediction of triaxial behavior of recycled aggregate concrete susing multivariable regression and artificial neural network techniques", Construct. Build. Mater., 226, 534-554. https://doi.org/10.1016/j.conbuildmat.2019.07.155.
  61. Xu, J., Liu, C., Huang, X., Zhang, Y., Zhou, H. and Lian, H. (2023), "Investigation of random fatigue life prediction based on artificial neural network", Steel Compos. Struct., 46(3), 435-449. https://doi.org/10.12989/scs.2023.46.3.435.
  62. Xu, J., Zhao, X., Yu, Y., Xie, T., Yang, G. and Xue, J. (2019b), "Parametric sensitivity analysis and modelling of mechanical properties of normal-and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks", Construct. Build. Mater., 211, 479-491. https://doi.org/10.1016/j.conbuildmat.2019.03.234.
  63. Yu, Y., Zhao, X., Xu, J., Chen, C., Deresa, S.T. and Zhang, J. (2020), "Machine learning-based evaluation of shear capacity of recycled aggregate concrete beams", Materials, 13(20), 4552. https://doi.org/10.3390/ma13204552.
  64. Zhao, W., Wang, L. and Mirjalili, S. (2022), "Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications", Comput. Meth. Appl. Mech. Eng., 388, 114194. https://doi.org/10.1016/j.cma.2021.114194.
  65. Zhao, X.Y., Chen, J.X., Chen, G.M., Xu, J.J. and Zhang, L.W. (2023), "Prediction of ultimate condition of FRP-confined recycled aggregate concrete using a hybrid boosting model enriched with tabular generative adversarial networks", Thin-Wall. Struct., 182, https://doi.org/110318.10.1016/j.tws.2022.110318.