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Research Article

A novel hybrid soft computing model using stacking with ensemble method for estimation of compressive strength of geopolymer composite

Pages 1494-1509 | Accepted 11 Jun 2021, Published online: 30 Jun 2021

References

  • Rovnaník P, Šimonová H, Topolář L, et al. Mechanical fracture properties of alkali-activated slag with graphite filler. Procedia Eng. 2017;190:43–48.
  • Palomo A, Grutzeck MW, Blanco MT. Alkali-activated fly ashes: a cement for the future, Cement and Concrete Research- 29,issue 8,1999;1323–1329.
  • Komnitsas KA. Potential of geopolymer technology towards green buildings and sustainable cities. Procedia Eng. 2011;21:1023–1032.
  • McLellan BC, Williams RP, Lay J, et al. Costs and carbon emissions for geopolymer pastes in comparison to ordinary portland cement. J Clean Prod. 2011;19(9–10):1080–1090. .
  • Bello, A CBDC, Cecchi A. Cecchi, Experiments on natural fibers: durability and mechanical properties. Adv Mater Process Technol. 2017;3(4):632–639.
  • R. T., A. P, K. K, et al. Mechanical properties and moisture behaviour of neem/banyan fibres reinforced with polymer matrix hybrid composite. Adv Mater Process Technol,Volume 7, issue 1 2021;1–12
  • Damilola OM. Syntheses, characterization and binding strength of geopolymers: a review. Int J Mat Sci Appl. 2013;2(6):185–193.
  • Rai B, Roy LB, Rajjak M. A statistical investigation of different parameters influencing compressive strength of fly ash induced geopolymer concrete. Struct Concr. 2018;19(5):1268–1279.
  • Guerrieri M, Sanjayan JG. Behavior of combined fly ash/slag‐based geopolymers when exposed to high temperatures. Fire Mater. 34,2010;163–175.
  • Anuradha R, Sreevidya V, Venkatasubramani R, et al. Modified guidelines for geopolymer concrete mix design using Indian standardAsian Journal of Civil Engineering 13(3), 2012 ;353-364.
  • Davidovits J, Geopolymers based on natural and synthetic metakaolin—a critical review, in Proceedings of the 41st International Conference on Advanced Ceramics and Composites, Wiley Online Library,Daytona Beach, Florida 2017; . 201–214.
  • Tchakouté HK, Rüscher CH, Metakaolin Based Geopolymer Cements from Commercial Sodium Waterglass and Sodium Waterglass from Rice Husk Ash: a Comparative Study, in Developments in Strategic Ceramic Materials II: 40th International Conference on Advanced Ceramics and Composites.37(7) Daytona, Florida, , 2017145-157.
  • Trindade ACC, Alcamand HA, Borges PHR, et al. Mechanical properties of jute fiber reinforced geopolymers. Adv Mat Sci Environ Ene Tech. 2017;VI:85–96.
  • Petrillo A, Cioffi R, De Felice F, et al. An environmental evaluation: a comparison between geopolymer and OPC concrete paving blocks manufacturing process in Italy. Envir Progress & Sustainable Energy . 2017 (35) 1699–1708.
  • Reed M, Lokuge W, Karunasena W. Fibre-reinforced geopolymer concrete with ambient curing for in situ applications. J Mater Sci. 2014;49(12):4297–4304.
  • Neupane K. High-strength geopolymer concrete properties. Advances in Materials 2018; 7(2): 15-25 .
  • Kumar S, Kumar R, Mehrotra SP. Influence of granulated blast furnace slag on the reaction, structure and properties of fly ash based geopolymer. J Mater Sci. 2010;45(3):607–615.
  • Panda B, Tan MJ. Experimental study on mix proportion and fresh properties of fly ash based geopolymer for 3D concrete printing. Ceram Int. 2018;44(9):10258–10265.
  • Ince R. Prediction of fracture parameters of concrete by artificial neural networks. Eng Fract Mech. 2004;71(15):2143–2159.
  • Nguyen KT, Nguyen QD, Le TA, et al. Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches. Constr Build Mater. 2020;(247):118581,1-11 .
  • Van Dao D, Ly H-B, Trinh SH, et al. Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials. 2019;12(6):983. .
  • Ben Chaabene W, Flah M, Nehdi ML. Machine learning prediction of mechanical properties of concrete: critical review. Constr Build Mater. 2020;(260):119889.
  • Rathakrishnan V, Beddu S, Ahmed AN, Comparison studies between machine learning optimisation technique on predicting concrete compressive strength, (2021).
  • Yaseen ZM, Deo RC, Hilal A, et al. Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Software. 2018;115:112–125.
  • Lahoti M, Narang P, Tan KH, et al. Mix design factors and strength prediction of metakaolin-based geopolymer. Ceram Int. 2017;43(14):11433–11441. .
  • Nazari A, Sanjayan JG. Modelling of compressive strength of geopolymer paste, mortar and concrete by optimized support vector machine. Ceram Int. 2015;41(9):12164–12177.
  • Nagajothi S, Elavenil S. Influence of aluminosilicate for the prediction of mechanical properties of geopolymer concrete–Artificial Neural Network. Silicon12, 2020;1011–1021.
  • Yadollahi MM, Benli A, Demirboğa R. Prediction of compressive strength of geopolymer composites using an artificial neural network. Mater Res Innovations. 2015;19(6):453–458.
  • Yadollahi MM, Benli A, Demirboga R. Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites. Neural Comput Appl. 2017;28(6):1453–1461.
  • Breiman CSL, Friedman J, Olshen R, Classification and regression tree, 1984.
  • Chanamarn N, Tamee K, Sittidech P, Stacking Technique for Academic Achievement Prediction, in 2016 International Workshop on Smart Info-Media Systems in Asia (SISA 2016),Ayutthaya,Thailand 2016: pp. 14–17.
  • Li Y, Zou C, Berecibar M, et al. Random forest regression for online capacity estimation of lithium-ion batteries. Appl Energy. 2018;232:197–210.
  • Rodriguez-Galiano V, Sánchez Castillo M, Chica-Olmo M, et al. learning predictive models for mineral prospectivity. An evaluation of neural networks, random forest, regression trees and support vector machines,Ore Geology Reviews,Volume 71,  2015, Pages 804-818.
  • Graczyk M, Lasota T, Trawiński B, et al. Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal, in Asian Conference on Intelligent Information and Database Systems, Springer-Verlag , Berlin, Heidelberg, 2010: pp. 340–350.
  • Ahmad MW, Mourshed M, Rezgui Y. Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression. Energy. 2018;164:465–474.
  • Ahmad MW, Reynolds J, Rezgui Y. Predictive modelling for solar thermal energy systems: a comparison of support vector regression, random forest, extra trees and regression trees. J Clean Prod. 2018;203(2018):810–821.
  • John V, Liu Z, Guo C, et al. Real-time lane estimation using deep features and extra trees regression, in image and video technology. Lecture Notes in Computer Science, vol 9431. Springer, Cham ; 2015, 721–733.
  • Friedman J, Hastie T, Tibshirani R, The elements of statistical learning, Springer series in statistics New York, 2001.
  • Mason L, Baxter J, Bartlett P, et al. Boosting algorithms as gradient descent in function space, in Nips, 1999.
  • Persson C, Bacher P, Shiga T, et al. Multi-site solar power forecasting using gradient boosted regression trees. Solar Energy. 2017;150:423–436.
  • Ting KM, Witten IH. Issues in stacked generalization. J Artif Intell Res. 1999;10:271–289.
  • Bhikshma V, Kumar TN. Mechanical properties of flyash based geopolymer concrete with the addition of GGBS. Sustain SolStructural Engineering Construction (SSEC). 2014; 451–456.
  • L G, Sheethal KT, Kumar WP, et al. Development of high strength geopolymer concrete using low molarity NaOH. Int J Eng Res. 2015;V4(7):194–200. .
  • Mehta A, Kumar K. Strength and durability characteristics of fly ash and slag based geopolymer concrete. Int J Civil Engineer Technol. 2016;7:305–314.
  • Shah A. Optimum utilization of GGBS in fly ash based geopolymer concrete.Kalpa Publications in Civil Engineering Volume 1, 2017 : 431–440  2018;1:.
  • Takekar A, Patail GR. Experimental study on mechanical properties of fly ash and GGBS based geopolymer concrete. IRJET,Volume: 04 Issue: 08 ,2017,18-23.  .
  • Goriparthi MR, Gunneswara Rao TD. Effect of fly ash and GGBS combination on mechanical and durability properties of GPC. Adv Concr Constr. 2017;5:313–330.
  • Geopolymer LK. Geopolymer concrete an eco-friendly construction material. Int J Res Eng Technol. 2014;03(23):164–167.
  • Reddy MS, Dinakar P, Rao BH. Mix design development of fly ash and ground granulated blast furnace slag based geopolymer concrete. J Building Engineer. 2018;20: 712–722.
  • Yanagibashi K, Yonezawa T, Sakuma M, et al. A study on green concrete. AIJ J Technol Design. 1995;1(1):61–66.
  • Wardhono A, Law DW, Sutikno, et al. The effect of slag addition on strength development of Class C fly ash geopolymer concrete at normal temperature, AIP Conference Proceedings: 1887, Indonesia 2017
  • Mallikarjuna Rao G, Gunneswara Rao TD. A quantitative method of approach in designing the mix proportions of fly ash and GGBS-based geopolymer concrete. Aust J Civil Engineer. 2018;16(1):53–63.
  • Sreenivasulu C, Jawahar JG, Sashidhar C. Predicting compressive strength of geopolymer concrete using NDT techniques. Asian J Civil Engineer. 2018;19(4):513–525.
  • Nagral MR, Ostwal T, Chitawadagi MV. Effect of curing temperature and curing hours on the properties of geopolymer concrete. Int J Comput Engin Res. 2014;4:1–11.
  • Guru Jawahar J, Lavanya D, Sashidhar C. Performance of fly ash and ggbs based geopolymer concrete in the acid environment. Int J Res Scientific Innovat. 2016;3:101–104.
  • Şahin Y, Şahin F. Effects of process factors on tribological behaviour of epoxy composites including Al2O3 nano particles: a comparative study on multi-regression analysis and artificial neural network. Adv Mater Process Technol. 2021;1–15.
  • Todorovski L, Džeroski S. Combining multiple models with meta decision trees, lecture notes in computer science Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science, vol 1910. Springer, Berlin, Heidelberg. (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics).2002;1910:54–64.
  • Aslam F, Farooq F, Amin MN, et al. Applications of gene expression programming for estimating compressive strength of high-strength concrete. Adv Civil Eng. 2020;(2020):8850535.
  • Han Q, Gui C, Xu J, et al. A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm. Construction and Building Materials. 226,2021:734–742. https://doi.org/10.1016/j.conbuildmat.2019.07.315
  • Pengcheng L, Xianguo W, Hongyu C, et al. Prediction of compressive strength of high-performance concrete by random forest algorithm, IOP Conference Series: Earth and Environmental Science. 552,Zhengzhou, China,2020 12020.
  • Young BA, Hall A, Pilon L, et al. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: new insights from statistical analysis and machine learning methods. Cement Concr Res. 2019;115(2019):379–388. .
  • Thai B, Duc M, Bui KT, et al. A novel artificial intelligence approach based on multi-layer perceptron neural network and biogeography-based optimization for predicting coefficient of consolidation of soil. Catena. 2019;173:302–311.
  • Golbraikh A, Tropsha A. Beware of q2! J Mol Graphics Modell. 2002;20:269–276.
  • Roy PP, Roy K. On some aspects of variable selection for partial least squares regression models. QSAR Combinatorial Sci. 2008;27:302–313.
  • Lu X, Zhou W, Ding X, et al. Ensemble learning regression for estimating unconfined compressive strength of cemented paste backfill. IEEE Access. 2019;7:72125–72133.
  • Marani A, Nehdi ML. Machine learning prediction of compressive strength for phase change materials integrated cementitious composites. Construction, Build Mat. 2020;265:120286.
  • Chou J-S, Pham T-PT, Nguyen T-K, et al. Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models. Soft Comput. 2020;24:3393–3411.
  • Aydogmus HY, Erdal HI, Karakurt O, et al. A comparative assessment of bagging ensemble models for modeling concrete slump flow. Comp Concrete. 2015;16:741–757.

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