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Bina Betonunun Karışım Oranı için Kaba-AHP ve MOORA Tabanlı Taguchi Optimizasyonu

Year 2023, Volume: 26 Issue: 4, 1307 - 1317, 01.12.2023

Salih Himmetoğlu Emel Kızılkaya Aydogan Fatih Özcan Okan Karahan Cengiz Atiş

https://doi.org/10.2339/politeknik.1018428
Cited By: 1

Abstract

İnşaat sektöründe inşaat betonu üretiminde kullanılan malzemeler ve bunların karışım oranları beton üreticileri için önemli bir karar verme ve optimizasyon problemidir. Yüksek dayanımlı ve fonksiyonel beton karışımı elde etmek için kullanılan malzemelerin miktarları (karışım oranları) her müşteri için ayrı ayrı belirlenen performans kriterlerini olumlu veya olumsuz etkileyebilir. Ayrıca her bir performans kriterinin beton üzerindeki etkisi ve önemi birbirinden farklı olabilir. Bu makale, müşteri taleplerine göre karar verilen performans kriterlerinin önem düzeylerini ve ağırlıklarını belirlemek için Kaba-AHP yaklaşımını ve müşteri talebi için betonun optimum karışım oranını hesaplamak için MOORA tabanlı Taguchi yaklaşımını önermektedir. Tüm deneysel tasarımların yapılmasından ziyade deney süresini ve maliyetini en aza indirecek deney tasarımının en iyi şekilde belirlenmesi amaçlanmaktadır.

Keywords

Taguchi metodu deney tasarımı kaba küme teorisi AHP MOORA

References

  • [1] Şimşek B., İç Y. T. and Şimşek E. H., “A TOPSIS-based Taguchi optimization to determine optimal mixture proportions of the high strength self-compacting concrete”, Chemometrics and Intelligent Laboratory Systems, 125, 18-32, (2013).
  • [2] Şimşek B., İç Y. T. and Şimşek E. H., “A TOPSIS-based Taguchi optimization to determine optimal mixture proportions of the high strength self-compacting concrete”, Chemometrics and Intelligent Laboratory Systems, 125, 18-32, (2013).
  • [3] Ozbay E., Oztas A., Baykasoglu A. and Ozbebek H., “Investigating mix proportions of high strength self-compacting concrete by using Taguchi method”, Construction and Building Materials, 23: (2), 694-702, (2009).
  • [4] Hınıslıoğlu S. and Bayrak O. Ü., “Optimization of early flexural strength of pavement concrete with silica fume and fly ash by the Taguchi method”, Civil Engineering and Environmental Systems, 21: (2), 79-90, (2004).
  • [5] Tanyildizi H. and Şahin M., “Application of Taguchi method for optimization of concrete strengthened with polymer after high temperature” Construction and Building Materials, 79: 97-103, (2015).
  • [6] Joshaghani A., Ramezanianpour A. A., Ataei O. and Golroo, A., “Optimizing pervious concrete pavement mixture design by using the Taguchi method”, Construction and Building Materials, 101: 317- 325, (2015).
  • [7] Hadi M. N., Farhan N. A. and Sheikh, M. N., “Design of geo-polymer concrete with GGBFS at ambient curing condition using Taguchi method”, Construction and Building Materials, 140: 424-431, (2017).
  • [8] Kate G. K. and Thakare S. B., “Optimization of sustainable fly ash concrete by Taguchi method for Indian construction industry”, Journal of Engineering Technology, 6: 151-159, (2018).
  • [9] Emara M. A., Eid F. M., Nasser A. A. and Safaan, M. A., “Prediction of Self-Compacting Rubberized Concrete Mechanical and Fresh Properties using Taguchi Method”, J Civil Environ Eng, 8: (301), 2, (2018).
  • [10] Ghazy M. F. and Abd El Hameed M. F., “Optimization of Lightweight Concrete Process by Gray-Taguchi Method”, ACI Materials Journal, 112: (3), (2015).
  • [11] Ertuğrul İ. and Karakaşoğlu N., “Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods”., Expert Systems with Applications, 36: (1), 702-715, (2009).
  • [12] de la Fuente A., Armengou J., Pons O. and Aguado, A., “Multi-criteria decision-making model for assessing the sustainability index of wind-turbine support systems: application to a new precast concrete alternative”, Journal of Civil Engineering and Management, 23: (2), 194-203, (2017).
  • [13] Şimşek B., İç Y. T. and Şimşek E. H., “Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems”, International Journal of Computational Intelligence Systems, 9: (3), 525-543, (2016).
  • [14] Şimşek B. and Uygunoğlu T., “Multi-response optimization of polymer blended concrete: A TOPSIS based Taguchi application”, Construction and Building Materials, 117: 251-262, (2016).
  • [15] Prusty J. K. and Pradhan B., “Multi-response optimization using Taguchi-Grey relational analysis for composition of fly ash-ground granulated blast furnace slag based geo-polymer concrete”, Construction and Building Materials, 241: 118049, (2020).
  • [16] Myers J. H. and Alpert M. I., “Determinant buying attitudes: meaning and measurement”, Journal of Marketing, 32: (4_part_1), 13-20 (1968).
  • [17] Saaty T. L., “The Analytic Hierarchy Process”, McGraw-Hill Inc., (1980).
  • [18] Meade L. M. and Sarkis J. J. I. J., “Analyzing organizational project alternatives for agile manufacturing processes: an analytical network approach”, International Journal of Production Research, 37: (2), 241-261, (1999).
  • [19] Aydogan E. K., “Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment”, Expert Systems with Applications, 38: (4), 3992-3998, (2011).
  • [20] Wang G., “Theory and knowledge acquisition of rough sets”. Xi'an Jiao tong University Publication, (2001).
  • [21] Yang T. and Chou P., “Solving a multi-response simulation-optimization problem with discrete variables using a multiple-attribute decision-making method”, Mathematics and Computers in Simulation, 68: (1), 9-21, (2005).
  • [22] Kuo Y., Yang T. and Huang G. W., “The use of a grey-based Taguchi method for optimizing multi-response simulation problems”, Engineering Optimization, 40: (6), 517-528, (2008).
  • [23] Chang C. Y., Huang R., Lee P. C. and Weng T. L., “Application of a weighted Grey-Taguchi method for optimizing recycled aggregate concrete mixtures”, Cement and Concrete Composites, 33: (10), 1038-1049, (2011).
  • [24] Tong L. I., Su C. T. and Wang C. H, “The optimization of multi‐response problems in the Taguchi method”, International Journal of Quality & Reliability Management, 14: (4), 367-380, (1997).
  • [25] Ferah M., “Çok Yanıtlı Taguchi Deneysel Tasarım Metodu ve Alüminyum Sanayinde Bir Uygulama”, Sakarya University Journal of Science, 7: (2), 61-69, (2003).

Rough-AHP and MOORA-based Taguchi Optimization for Mixture Proportion of Building Concrete

Year 2023, Volume: 26 Issue: 4, 1307 - 1317, 01.12.2023

Salih Himmetoğlu Emel Kızılkaya Aydogan Fatih Özcan Okan Karahan Cengiz Atiş

https://doi.org/10.2339/politeknik.1018428
Cited By: 1

Abstract

In the construction industry, materials that are used for producing construction concrete and their mixture proportions are an important decision-making and optimization problem for concrete manufacturers. Amounts (mixture proportions) of the materials, which are used in order to obtain a mixture of high strength and functional concrete, can positively or negatively affect performance criteria which are individually determined for each customer. Furthermore, the effect and significance of each performance criterion on the concrete may differ from each other. This paper proposes the Rough-AHP approach to determine the significance levels and weights of the performance criteria decided according to the customer demands and the MOORA-based Taguchi approach to calculate the optimal mixture proportion of the concrete for the customer demand. It is aimed to determine the best design of the experiment to minimize the experiment duration and cost rather than to perform all experimental designs.

Keywords

Taguchi method design of experiment rough set theory AHP MOORA

References

  • [1] Şimşek B., İç Y. T. and Şimşek E. H., “A TOPSIS-based Taguchi optimization to determine optimal mixture proportions of the high strength self-compacting concrete”, Chemometrics and Intelligent Laboratory Systems, 125, 18-32, (2013).
  • [2] Şimşek B., İç Y. T. and Şimşek E. H., “A TOPSIS-based Taguchi optimization to determine optimal mixture proportions of the high strength self-compacting concrete”, Chemometrics and Intelligent Laboratory Systems, 125, 18-32, (2013).
  • [3] Ozbay E., Oztas A., Baykasoglu A. and Ozbebek H., “Investigating mix proportions of high strength self-compacting concrete by using Taguchi method”, Construction and Building Materials, 23: (2), 694-702, (2009).
  • [4] Hınıslıoğlu S. and Bayrak O. Ü., “Optimization of early flexural strength of pavement concrete with silica fume and fly ash by the Taguchi method”, Civil Engineering and Environmental Systems, 21: (2), 79-90, (2004).
  • [5] Tanyildizi H. and Şahin M., “Application of Taguchi method for optimization of concrete strengthened with polymer after high temperature” Construction and Building Materials, 79: 97-103, (2015).
  • [6] Joshaghani A., Ramezanianpour A. A., Ataei O. and Golroo, A., “Optimizing pervious concrete pavement mixture design by using the Taguchi method”, Construction and Building Materials, 101: 317- 325, (2015).
  • [7] Hadi M. N., Farhan N. A. and Sheikh, M. N., “Design of geo-polymer concrete with GGBFS at ambient curing condition using Taguchi method”, Construction and Building Materials, 140: 424-431, (2017).
  • [8] Kate G. K. and Thakare S. B., “Optimization of sustainable fly ash concrete by Taguchi method for Indian construction industry”, Journal of Engineering Technology, 6: 151-159, (2018).
  • [9] Emara M. A., Eid F. M., Nasser A. A. and Safaan, M. A., “Prediction of Self-Compacting Rubberized Concrete Mechanical and Fresh Properties using Taguchi Method”, J Civil Environ Eng, 8: (301), 2, (2018).
  • [10] Ghazy M. F. and Abd El Hameed M. F., “Optimization of Lightweight Concrete Process by Gray-Taguchi Method”, ACI Materials Journal, 112: (3), (2015).
  • [11] Ertuğrul İ. and Karakaşoğlu N., “Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods”., Expert Systems with Applications, 36: (1), 702-715, (2009).
  • [12] de la Fuente A., Armengou J., Pons O. and Aguado, A., “Multi-criteria decision-making model for assessing the sustainability index of wind-turbine support systems: application to a new precast concrete alternative”, Journal of Civil Engineering and Management, 23: (2), 194-203, (2017).
  • [13] Şimşek B., İç Y. T. and Şimşek E. H., “Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems”, International Journal of Computational Intelligence Systems, 9: (3), 525-543, (2016).
  • [14] Şimşek B. and Uygunoğlu T., “Multi-response optimization of polymer blended concrete: A TOPSIS based Taguchi application”, Construction and Building Materials, 117: 251-262, (2016).
  • [15] Prusty J. K. and Pradhan B., “Multi-response optimization using Taguchi-Grey relational analysis for composition of fly ash-ground granulated blast furnace slag based geo-polymer concrete”, Construction and Building Materials, 241: 118049, (2020).
  • [16] Myers J. H. and Alpert M. I., “Determinant buying attitudes: meaning and measurement”, Journal of Marketing, 32: (4_part_1), 13-20 (1968).
  • [17] Saaty T. L., “The Analytic Hierarchy Process”, McGraw-Hill Inc., (1980).
  • [18] Meade L. M. and Sarkis J. J. I. J., “Analyzing organizational project alternatives for agile manufacturing processes: an analytical network approach”, International Journal of Production Research, 37: (2), 241-261, (1999).
  • [19] Aydogan E. K., “Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment”, Expert Systems with Applications, 38: (4), 3992-3998, (2011).
  • [20] Wang G., “Theory and knowledge acquisition of rough sets”. Xi'an Jiao tong University Publication, (2001).
  • [21] Yang T. and Chou P., “Solving a multi-response simulation-optimization problem with discrete variables using a multiple-attribute decision-making method”, Mathematics and Computers in Simulation, 68: (1), 9-21, (2005).
  • [22] Kuo Y., Yang T. and Huang G. W., “The use of a grey-based Taguchi method for optimizing multi-response simulation problems”, Engineering Optimization, 40: (6), 517-528, (2008).
  • [23] Chang C. Y., Huang R., Lee P. C. and Weng T. L., “Application of a weighted Grey-Taguchi method for optimizing recycled aggregate concrete mixtures”, Cement and Concrete Composites, 33: (10), 1038-1049, (2011).
  • [24] Tong L. I., Su C. T. and Wang C. H, “The optimization of multi‐response problems in the Taguchi method”, International Journal of Quality & Reliability Management, 14: (4), 367-380, (1997).
  • [25] Ferah M., “Çok Yanıtlı Taguchi Deneysel Tasarım Metodu ve Alüminyum Sanayinde Bir Uygulama”, Sakarya University Journal of Science, 7: (2), 61-69, (2003).
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Review Article
Authors

Salih Himmetoğlu 0000-0001-6081-3650

Emel Kızılkaya Aydogan 0000-0003-0927-6698

Fatih Özcan 0000-0003-3391-9411

Okan Karahan 0000-0001-7970-1982

Cengiz Atiş 0000-0003-3459-329X

Publication Date December 1, 2023
Submission Date November 5, 2021
Published in Issue Year 2023 Volume: 26 Issue: 4

Cite

APA Himmetoğlu, S., Kızılkaya Aydogan, E., Özcan, F., Karahan, O., et al. (2023). Rough-AHP and MOORA-based Taguchi Optimization for Mixture Proportion of Building Concrete. Politeknik Dergisi, 26(4), 1307-1317. https://doi.org/10.2339/politeknik.1018428
AMA Himmetoğlu S, Kızılkaya Aydogan E, Özcan F, Karahan O, Atiş C. Rough-AHP and MOORA-based Taguchi Optimization for Mixture Proportion of Building Concrete. Politeknik Dergisi. December 2023;26(4):1307-1317. doi:10.2339/politeknik.1018428
Chicago Himmetoğlu, Salih, Emel Kızılkaya Aydogan, Fatih Özcan, Okan Karahan, and Cengiz Atiş. “Rough-AHP and MOORA-Based Taguchi Optimization for Mixture Proportion of Building Concrete”. Politeknik Dergisi 26, no. 4 (December 2023): 1307-17. https://doi.org/10.2339/politeknik.1018428.
EndNote Himmetoğlu S, Kızılkaya Aydogan E, Özcan F, Karahan O, Atiş C (December 1, 2023) Rough-AHP and MOORA-based Taguchi Optimization for Mixture Proportion of Building Concrete. Politeknik Dergisi 26 4 1307–1317.
IEEE S. Himmetoğlu, E. Kızılkaya Aydogan, F. Özcan, O. Karahan, and C. Atiş, “Rough-AHP and MOORA-based Taguchi Optimization for Mixture Proportion of Building Concrete”, Politeknik Dergisi, vol. 26, no. 4, pp. 1307–1317, 2023, doi: 10.2339/politeknik.1018428.
ISNAD Himmetoğlu, Salih et al. “Rough-AHP and MOORA-Based Taguchi Optimization for Mixture Proportion of Building Concrete”. Politeknik Dergisi 26/4 (December 2023), 1307-1317. https://doi.org/10.2339/politeknik.1018428.
JAMA Himmetoğlu S, Kızılkaya Aydogan E, Özcan F, Karahan O, Atiş C. Rough-AHP and MOORA-based Taguchi Optimization for Mixture Proportion of Building Concrete. Politeknik Dergisi. 2023;26:1307–1317.
MLA Himmetoğlu, Salih et al. “Rough-AHP and MOORA-Based Taguchi Optimization for Mixture Proportion of Building Concrete”. Politeknik Dergisi, vol. 26, no. 4, 2023, pp. 1307-1, doi:10.2339/politeknik.1018428.
Vancouver Himmetoğlu S, Kızılkaya Aydogan E, Özcan F, Karahan O, Atiş C. Rough-AHP and MOORA-based Taguchi Optimization for Mixture Proportion of Building Concrete. Politeknik Dergisi. 2023;26(4):1307-1.

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