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Year 2020, , 482 - 492, 25.06.2020

Kasım Mermerdaş , Süleyman İpek , Muhammet Burak Bozgeyik

https://doi.org/10.17776/csj.589207
Cited By: 2

Abstract

References

  • [1] BS EN 197–1, Cement: Composition, Specifications and Conformity Criteria for Common Cements British Standards Institution,London, 2000.
  • [2] Hirschi T., Sika Concrete Handbook Sika Services AG, Zurique, Suíça, 2005.
  • [3] Mermerdaş K., Gesoğlu M., Güneyisi E., Özturan T., Strength development of concretes incorporated with metakaolin and different types of calcined kaolins. Constr. Build. Mater., 37 (2012) 766-774.
  • [4] Sayed M. A., Statistical modelling and prediction of compressive strength of concrete.Concrete. Research Letters, 3(2) (2003) 452-458.
  • [5] Deshpande N., Londhe S., Kulkarni S., Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression, International Journal of Sustainable Built Environment.3(2) (2014) 187–198.
  • [6] Chandwani V., Agrawal V., Nagar R. Applications of Artificial Neural Networks in Modeling Compressive Strength of Concrete: A State of the Art Review. International Journal of Current Engineering and Technology, 4 (2014) 2949-2956.
  • [7] Topçu İ.B., Boğa A.R., Hocaoğlu FO., Modeling corrosion currents of reinforced concrete using ANN. Automat. in Constr., 18(2) (2009) 145–52.
  • [8] Lim C.H., Yoon Y.S., Kim J.H., Genetic algorithm in mix proportioning of high performance concrete. Cem. Concr. Res., 34(3) (2004) 409–20.
  • [9] Fairbairn E.M.R., Silvoso M.M., Filho R.D.T, Alves J.L.D., Ebecken N.F.F. Optimization of mass concrete construction using genetic algorithms. Comput. Struct., 82(2-3) (2004) 281–99.
  • [10] Özcan F., Atis C., Karahan O., Uncuoglu E., Tanyildizi H., Comparison of artificial neural network and fuzzy logic models for prediction of long term compressive strength of silica fume concrete, Advances in Engineering Software, 40 (2009) 856–863.
  • [11] İnan G., Göktepe A.B., Ramyar K., Sezer A. Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology. Build Environ 42(3) (2007) 1264–69.
  • [12] Shahin M.A., Maier H.R. and Jaksa M.B., Predicting settlement of Shallow Foundations using Neural Networks, Journal of Geotechnical and Geoenvironmental Engineering, 128(9) (2002) 785-793.
  • [13] Topçu İ.B., Sarıdemir M., Prediction of properties of waste AAC aggregate concrete using artificial neural network. Comput. Mater. Sci., 41(1) (2007) 117–25.
  • [14] Topçu İ.B., Sarıdemir M., Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput. Mater. Sci.;41(3) (2008) 305–11.
  • [15] Adhikary B.B., Mutsuyoshi H., Prediction of shear strength of steel fiber RC beams using neural networks. Constr. Build. Mater., 20(9) (2006) 801–11.
  • [16] Mermerdaş K., Güneyisi E., Gesoğlu M., Özturan T., Experimental evaluation and modeling of drying shrinkage behavior of metakaolin and calcined kaolin blended concretes Constr. Build. Mater., 43 (2013) 337-347.
  • [17] Duan Z.H., Kou S.C, Poon C.S., Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Constr. Build. Mater. 40 (2013) 1200–06.
  • [18] Tokar, A.S. and Johnson, P.A., Rainfall-Runoff Modeling Using Artificial Neural Networks, J. Hydrol. Eng., 4(3) (1999) 232- 239.
  • [19] Ashour, A.F., Alvarez L.F., Toropov, V.V., Empirical modeling of shear strength RC deep beams by genetic programming. Comput. Struct., 81(5) (2003) 331–38.
  • [20] Sarıdemir, M., Effect of specimen size and shape on compressive strength of concrete containing fly ash: Application of genetic programming for design, Materials and Design, 56 (2014) 297-304
  • [21] Lee, S.C. and Han, S.W., Neural-network-based models for generating artificial earthquakes and response spectra. Computers and Structures 80 (2002) 1627–1638.
  • [22] D'Aniello M., Mete-Güneyisi, E., Landolfo, R., Mermerdaş, K., Predictive Models of the Flexural Overstrength Factor for Steel Thin-Walled Circular Hollow Section Beams, Thin-Walled Structures, 94 (2015)67-78.
  • [23] Mete-Güneyisi E., Gesoğlu, M., Güneyisi, E., Mermerdaş, K., Assessment of Shear Capacity of Adhesive Anchors for Structures Using Neural Network Based Model, Materials and Structures, (2015) DOI 10.1617/s11527-015-0558-x.
  • [24] Mete-Güneyisi, E., D'Aniello M., Landolfo, R., Mermerdaş, K., Prediction of the Flexural Overstrength Factor for Steel Beams Using Artificial Neural Network, Steel and Composite Structures, 17 (2014) 215-236.
  • [25] D'Aniello M., Mete-Güneyisi, E., Landolfo, R., Mermerdaş, K., Analytical Prediction of Available Rotation Capacity of Cold-Formed Rectangular and Square Hollow Section Beams, Thin-Walled Structures, 77 (2014) 141-152.
  • [26] Gesoğlu M., Mete-Güneyisi E., Güneyisi E., Yılmaz E., Mermerdaş K., Modeling and analysis of the shear capacity of adhesive anchors post-installed into uncracked concrete. Composites Part B: Engineering, 60 (2014) 716-724.
  • [27] ASTM C39/C39M-12, Standard test method for compressive strength of cylindrical concrete specimens. Annual book of ASTM Standards, 2012.
  • [28] Ferreira C., Gene expression programming; a new adaptive algorithm for solving problems. Complex Syst., 12(2) (2001) 87-129.
  • [29] Li X., Zhou C., Xiao W., Nelson P.C., Prefix gene expression programming. in Late Breaking Paper at the Genetic and Evolutionary Computation Conference (GECCO), Washington, D.C., 2005.
  • [30] Koza J.R. Genetic programming; on the programming of computers by means of natural selection, MIT Press, USA, 1992.
  • [31] Gen M, Cheng R. Genetic algorithms and engineering design, Wiley, USA, 1997.

Experimental evaluation and modeling of the compressive strength of concretes with various strength classes of cements

Year 2020, , 482 - 492, 25.06.2020

Kasım Mermerdaş , Süleyman İpek , Muhammet Burak Bozgeyik

https://doi.org/10.17776/csj.589207
Cited By: 2

Abstract

This study aimed to
propose a prediction model for estimation of strength of concretes with various
cements and mixture proportions. The strength of the samples produced with
three different types of cement at different rates of water-to-cement ratios
and cement richness were investigated experimentally and evaluated statistically.
Three type of cement possessing 28-day strengths of 32.5, 42.5, and 52.5 MPa
was used in the production of concretes. The concretes were produced at cement richness
values of 300, 400, and 500 kg/m3 and w/c rates at changing levels within
the interval of between 0.3 and 0.6. By this way, combined influences of cement
strength, amount of cement and w/c ratio was experimentally investigated. Totally
36 mixes were cast then the compressive strength values were examined after specified
moist curing periods (7 and 28 day). A statistical study were conducted on the
experimental results and the significances of the cement strength, w/c values
and amount of cement on the compressive strength of the concretes were assessed.
Another crucial focus of the current paper is to generate an explicit
expression to predict the compressive strength of the concretes tackled with
the current study. To derive an explicit formula for estimation, a soft
computing method called gene expression programming (GEP) was benefited. The
GEP model was also compared with a less complicated estimation model developed
by multi linear regression method. The results revealed that compressive
strength of the samples were significantly influenced by cement type and
aggregate-to-cement ratio. The proposed GEP model indicated a high correlation
between experimental and predicted values.

Keywords

Compressive strength Gene Expression Programming Multiple Linear Regression Statistical evaluation

References

  • [1] BS EN 197–1, Cement: Composition, Specifications and Conformity Criteria for Common Cements British Standards Institution,London, 2000.
  • [2] Hirschi T., Sika Concrete Handbook Sika Services AG, Zurique, Suíça, 2005.
  • [3] Mermerdaş K., Gesoğlu M., Güneyisi E., Özturan T., Strength development of concretes incorporated with metakaolin and different types of calcined kaolins. Constr. Build. Mater., 37 (2012) 766-774.
  • [4] Sayed M. A., Statistical modelling and prediction of compressive strength of concrete.Concrete. Research Letters, 3(2) (2003) 452-458.
  • [5] Deshpande N., Londhe S., Kulkarni S., Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression, International Journal of Sustainable Built Environment.3(2) (2014) 187–198.
  • [6] Chandwani V., Agrawal V., Nagar R. Applications of Artificial Neural Networks in Modeling Compressive Strength of Concrete: A State of the Art Review. International Journal of Current Engineering and Technology, 4 (2014) 2949-2956.
  • [7] Topçu İ.B., Boğa A.R., Hocaoğlu FO., Modeling corrosion currents of reinforced concrete using ANN. Automat. in Constr., 18(2) (2009) 145–52.
  • [8] Lim C.H., Yoon Y.S., Kim J.H., Genetic algorithm in mix proportioning of high performance concrete. Cem. Concr. Res., 34(3) (2004) 409–20.
  • [9] Fairbairn E.M.R., Silvoso M.M., Filho R.D.T, Alves J.L.D., Ebecken N.F.F. Optimization of mass concrete construction using genetic algorithms. Comput. Struct., 82(2-3) (2004) 281–99.
  • [10] Özcan F., Atis C., Karahan O., Uncuoglu E., Tanyildizi H., Comparison of artificial neural network and fuzzy logic models for prediction of long term compressive strength of silica fume concrete, Advances in Engineering Software, 40 (2009) 856–863.
  • [11] İnan G., Göktepe A.B., Ramyar K., Sezer A. Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology. Build Environ 42(3) (2007) 1264–69.
  • [12] Shahin M.A., Maier H.R. and Jaksa M.B., Predicting settlement of Shallow Foundations using Neural Networks, Journal of Geotechnical and Geoenvironmental Engineering, 128(9) (2002) 785-793.
  • [13] Topçu İ.B., Sarıdemir M., Prediction of properties of waste AAC aggregate concrete using artificial neural network. Comput. Mater. Sci., 41(1) (2007) 117–25.
  • [14] Topçu İ.B., Sarıdemir M., Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput. Mater. Sci.;41(3) (2008) 305–11.
  • [15] Adhikary B.B., Mutsuyoshi H., Prediction of shear strength of steel fiber RC beams using neural networks. Constr. Build. Mater., 20(9) (2006) 801–11.
  • [16] Mermerdaş K., Güneyisi E., Gesoğlu M., Özturan T., Experimental evaluation and modeling of drying shrinkage behavior of metakaolin and calcined kaolin blended concretes Constr. Build. Mater., 43 (2013) 337-347.
  • [17] Duan Z.H., Kou S.C, Poon C.S., Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Constr. Build. Mater. 40 (2013) 1200–06.
  • [18] Tokar, A.S. and Johnson, P.A., Rainfall-Runoff Modeling Using Artificial Neural Networks, J. Hydrol. Eng., 4(3) (1999) 232- 239.
  • [19] Ashour, A.F., Alvarez L.F., Toropov, V.V., Empirical modeling of shear strength RC deep beams by genetic programming. Comput. Struct., 81(5) (2003) 331–38.
  • [20] Sarıdemir, M., Effect of specimen size and shape on compressive strength of concrete containing fly ash: Application of genetic programming for design, Materials and Design, 56 (2014) 297-304
  • [21] Lee, S.C. and Han, S.W., Neural-network-based models for generating artificial earthquakes and response spectra. Computers and Structures 80 (2002) 1627–1638.
  • [22] D'Aniello M., Mete-Güneyisi, E., Landolfo, R., Mermerdaş, K., Predictive Models of the Flexural Overstrength Factor for Steel Thin-Walled Circular Hollow Section Beams, Thin-Walled Structures, 94 (2015)67-78.
  • [23] Mete-Güneyisi E., Gesoğlu, M., Güneyisi, E., Mermerdaş, K., Assessment of Shear Capacity of Adhesive Anchors for Structures Using Neural Network Based Model, Materials and Structures, (2015) DOI 10.1617/s11527-015-0558-x.
  • [24] Mete-Güneyisi, E., D'Aniello M., Landolfo, R., Mermerdaş, K., Prediction of the Flexural Overstrength Factor for Steel Beams Using Artificial Neural Network, Steel and Composite Structures, 17 (2014) 215-236.
  • [25] D'Aniello M., Mete-Güneyisi, E., Landolfo, R., Mermerdaş, K., Analytical Prediction of Available Rotation Capacity of Cold-Formed Rectangular and Square Hollow Section Beams, Thin-Walled Structures, 77 (2014) 141-152.
  • [26] Gesoğlu M., Mete-Güneyisi E., Güneyisi E., Yılmaz E., Mermerdaş K., Modeling and analysis of the shear capacity of adhesive anchors post-installed into uncracked concrete. Composites Part B: Engineering, 60 (2014) 716-724.
  • [27] ASTM C39/C39M-12, Standard test method for compressive strength of cylindrical concrete specimens. Annual book of ASTM Standards, 2012.
  • [28] Ferreira C., Gene expression programming; a new adaptive algorithm for solving problems. Complex Syst., 12(2) (2001) 87-129.
  • [29] Li X., Zhou C., Xiao W., Nelson P.C., Prefix gene expression programming. in Late Breaking Paper at the Genetic and Evolutionary Computation Conference (GECCO), Washington, D.C., 2005.
  • [30] Koza J.R. Genetic programming; on the programming of computers by means of natural selection, MIT Press, USA, 1992.
  • [31] Gen M, Cheng R. Genetic algorithms and engineering design, Wiley, USA, 1997.
There are 31 citations in total.

Details

Primary Language English
Journal Section Engineering Sciences
Authors

Kasım Mermerdaş 0000-0002-1274-6016

Süleyman İpek 0000-0001-8891-949X

Muhammet Burak Bozgeyik 0000-0002-5124-5046

Publication Date June 25, 2020
Submission Date July 9, 2019
Acceptance Date April 15, 2020
Published in Issue Year 2020

Cite

APA Mermerdaş, K., İpek, S., & Bozgeyik, M. B. (2020). Experimental evaluation and modeling of the compressive strength of concretes with various strength classes of cements. Cumhuriyet Science Journal, 41(2), 482-492. https://doi.org/10.17776/csj.589207

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Hybrid artificial neural network with biogeography-based optimization to assess the role of cement fineness on ecological footprint and mechanical properties of cement mortar expose to freezing/thawing
Construction and Building Materials
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https://doi.org/10.1016/j.conbuildmat.2021.124589