Araştırma Makalesi
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Yıl 2023, Cilt: 41 Sayı: 6, 1088 - 1095, 29.12.2023

Öz

Kaynakça

  • REFERENCES
  • [1] Yeh IC. Modeling of strength of high performance concrete using artificial neural networks. Cement Concrete Res. 1998;28:1797–1808. [CrossRef]
  • [2] Abrams DA. Design of concrete mixtures. Bull Struct Mater Res Lab Lewis Inst Chic. 1919;1:25.
  • [3] Popovics S. Analysis of concrete strength versus water-cement ratio relationship. ACI Mater J. 1990;87:517–29. [CrossRef]
  • [4] Yeh IC. Modeling Concrete Strength with Augment-Neuron Networks. J Mater Civ Eng. 1998;10:263–268. [CrossRef]
  • [5] Yeh IC. Analysis of strength of concrete using design of experiments and neural networks. J Mater Civ Eng. 2006;18:597–604. [CrossRef]
  • [6] Silva PF, Moita GF, Arruda VF. A Computational Method to Predict the Concrete Compression Strength Using Decision Trees and Random Forest. In: XLI Ibero-Latin-American Congress on Computational Methods in Engineering, Brazil; November 2020.
  • [7] Ozturan M, Kutlu B, Ozturan T. Comparison of concrete strength prediction techniques with artificial neural network approach. Build Res J. 2008:56;23–36.
  • [8] Ahmad M, Hu JL, Ahmad F, Tang XW, Amjad M, Iqbal MJ, et al. Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature. Materials. 2021;14:1983. [CrossRef]
  • [9] Sojobi AO, Aladegboye OJ, Awolusi TF. Green interlocking paving units. Constr Build Mater. 2018;173:600–614. [CrossRef]
  • [10] UCI Machine Learning Repository. Concrete Compressive Strength. Available at: http://archive.ics.uci.edu/ml/dataset/concrete+compressive+strength. Accessed March 30, 2021.
  • [11] Pawlak Z. Rough Sets. Int J Comput Inf Sci. 1982;11:341–356. [CrossRef]
  • [12] Pawlak Z. Rough Sets and Flow Graphs. Lect Notes Comput Sci. 2005;3641:1–11. [CrossRef]
  • [13] Cui N, Cui Q. A Rough-Set Based Approach to Predict Consumers' Brand Preference. In: Proceedings of the 2009 International Workshop on Intelligent Systems and Applications; 2009. Wuhan, China. p. 1-4. [CrossRef]
  • [14] Grzymala-Busse JW. Rule Induction. In: Maimon O, Rokach L, editors. Data Mining and Knowledge Discovery Handbook. Boston: Springer; 2009. p. 249–265. [CrossRef]
  • [15] Liou JJH, Tzeng G. A Dominance-based Rough Set Approach to Customer Behavior in the Airline Market. Inf Sci. 2010;180:2230–2238. [CrossRef]
  • [16] Greco S, Matarazzo B, Slowinski R. Rough approximation by dominance relations. Int J Intell Syst. 2002;17:153–171. [CrossRef]
  • [17] Greco S, Matarazzo B, Slowinski R. Multicriteria classification by dominance-based rough set approach. Università degli Studi. 2000:1–14.
  • [18] Greco S, Matarazzo B, Slowinski R. Rough sets theory for multicriteria decision analysis. Eur J Oper Res. 2001;129:1–47. [CrossRef]
  • [19] Greco S, Matarazzo B, Slowinski R, Stefanowski J. An algorithm for induction of decision rules consistent with the dominance principle. In: Rough Sets and Current Trends in Computing, Second International Conference, RSCTC 2000, Banff, Canada, October 16-19, 2000, Revised Papers, 304–313. [CrossRef]
  • [20] Slowinski R, Greco S, Matarazzo B. Rough set and rule based multicriteria decision aiding. Pesqui Oper. 2012;32:213–270. [CrossRef]
  • [21] Chakhar S, Ishizaka A, Labib A, Saad I. Dominance-based rough set approach for group decisions. Eur J Oper Res. 2016;251:206–224. [CrossRef]
  • [22] Hu Q, Chakhar S, Siraj S, Labib A. Spare parts classification in industrial manufacturing using the dominance-based rough set approach. Eur J Oper Res. 2017;262:1136–1163. [CrossRef]
  • [23] BŁaszczyński J, Greco S, SŁowiński R. Inductive discovery of laws using monotonic rules. Eng Appl Artif Intell. 2012;25:284–294. [CrossRef]
  • [24] BŁaszczyński J, Greco S, SŁowiński R. Multi-criteria classification–A new scheme for application of dominance-based decision rules. Eur J Oper Res. 2007;181:1030–1044. [CrossRef]
  • [25] Abdellatif S, Hassine MAB, Yahia SB, Bouzeghoub A. ARCID: a new approach to deal with imbalanced datasets classification. In: International Conference on Current Trends in Theory and Practice of Informatics; 2018. p. 569–580. [CrossRef]
  • [26] Krawczyk B. Learning from imbalanced data: open challenges and future directions. Prog Artif Intell. 2016;5:221–232. [CrossRef]
  • [27] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357. [CrossRef]
  • [28] JAMM Software Tool. Available at: http://idss.cs.put.poznan.pl/site/75.html. Accessed April 12, 2021.

Prediction of compressive strength class of concrete with dominance based rough set approach*

Yıl 2023, Cilt: 41 Sayı: 6, 1088 - 1095, 29.12.2023

Öz

Dominance based rough set approach is important in studies conducted with datasets con-taining uncertainty. In this study, a dataset consisting of 1030 samples obtained in the laboratory regarding compressive strength of concrete has been considered. The decision attribute, which has continuous values, has been made discrete for applying dominance relation. In order to measure performance, samples in the dataset have been divided into two groups: the training set and the testing set. This process has been done in a way that corresponds to the distribution of each class within the dataset. On the other hand, since there is a class which has more or less samples than the others, synthetic data generation has been done with Synthetic Minority Oversampling Technique (SMOTE) in order to handle the between-class imbalance problem and equalize the number of samples in the classes. As a result, the training set has been made perfectly balanced. A decision-support model which extracts “if… then…” exact decision rules has been designed to be used in determining the quality or compressive strength of the concrete samples by using dominance based rough set approach. Performance of these rules on the testing set through the confusion matrix has been discussed. The exper-imental results show that performance of the exact decision rules induced by the dominance rough set approach on the testing set is significant.

Kaynakça

  • REFERENCES
  • [1] Yeh IC. Modeling of strength of high performance concrete using artificial neural networks. Cement Concrete Res. 1998;28:1797–1808. [CrossRef]
  • [2] Abrams DA. Design of concrete mixtures. Bull Struct Mater Res Lab Lewis Inst Chic. 1919;1:25.
  • [3] Popovics S. Analysis of concrete strength versus water-cement ratio relationship. ACI Mater J. 1990;87:517–29. [CrossRef]
  • [4] Yeh IC. Modeling Concrete Strength with Augment-Neuron Networks. J Mater Civ Eng. 1998;10:263–268. [CrossRef]
  • [5] Yeh IC. Analysis of strength of concrete using design of experiments and neural networks. J Mater Civ Eng. 2006;18:597–604. [CrossRef]
  • [6] Silva PF, Moita GF, Arruda VF. A Computational Method to Predict the Concrete Compression Strength Using Decision Trees and Random Forest. In: XLI Ibero-Latin-American Congress on Computational Methods in Engineering, Brazil; November 2020.
  • [7] Ozturan M, Kutlu B, Ozturan T. Comparison of concrete strength prediction techniques with artificial neural network approach. Build Res J. 2008:56;23–36.
  • [8] Ahmad M, Hu JL, Ahmad F, Tang XW, Amjad M, Iqbal MJ, et al. Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature. Materials. 2021;14:1983. [CrossRef]
  • [9] Sojobi AO, Aladegboye OJ, Awolusi TF. Green interlocking paving units. Constr Build Mater. 2018;173:600–614. [CrossRef]
  • [10] UCI Machine Learning Repository. Concrete Compressive Strength. Available at: http://archive.ics.uci.edu/ml/dataset/concrete+compressive+strength. Accessed March 30, 2021.
  • [11] Pawlak Z. Rough Sets. Int J Comput Inf Sci. 1982;11:341–356. [CrossRef]
  • [12] Pawlak Z. Rough Sets and Flow Graphs. Lect Notes Comput Sci. 2005;3641:1–11. [CrossRef]
  • [13] Cui N, Cui Q. A Rough-Set Based Approach to Predict Consumers' Brand Preference. In: Proceedings of the 2009 International Workshop on Intelligent Systems and Applications; 2009. Wuhan, China. p. 1-4. [CrossRef]
  • [14] Grzymala-Busse JW. Rule Induction. In: Maimon O, Rokach L, editors. Data Mining and Knowledge Discovery Handbook. Boston: Springer; 2009. p. 249–265. [CrossRef]
  • [15] Liou JJH, Tzeng G. A Dominance-based Rough Set Approach to Customer Behavior in the Airline Market. Inf Sci. 2010;180:2230–2238. [CrossRef]
  • [16] Greco S, Matarazzo B, Slowinski R. Rough approximation by dominance relations. Int J Intell Syst. 2002;17:153–171. [CrossRef]
  • [17] Greco S, Matarazzo B, Slowinski R. Multicriteria classification by dominance-based rough set approach. Università degli Studi. 2000:1–14.
  • [18] Greco S, Matarazzo B, Slowinski R. Rough sets theory for multicriteria decision analysis. Eur J Oper Res. 2001;129:1–47. [CrossRef]
  • [19] Greco S, Matarazzo B, Slowinski R, Stefanowski J. An algorithm for induction of decision rules consistent with the dominance principle. In: Rough Sets and Current Trends in Computing, Second International Conference, RSCTC 2000, Banff, Canada, October 16-19, 2000, Revised Papers, 304–313. [CrossRef]
  • [20] Slowinski R, Greco S, Matarazzo B. Rough set and rule based multicriteria decision aiding. Pesqui Oper. 2012;32:213–270. [CrossRef]
  • [21] Chakhar S, Ishizaka A, Labib A, Saad I. Dominance-based rough set approach for group decisions. Eur J Oper Res. 2016;251:206–224. [CrossRef]
  • [22] Hu Q, Chakhar S, Siraj S, Labib A. Spare parts classification in industrial manufacturing using the dominance-based rough set approach. Eur J Oper Res. 2017;262:1136–1163. [CrossRef]
  • [23] BŁaszczyński J, Greco S, SŁowiński R. Inductive discovery of laws using monotonic rules. Eng Appl Artif Intell. 2012;25:284–294. [CrossRef]
  • [24] BŁaszczyński J, Greco S, SŁowiński R. Multi-criteria classification–A new scheme for application of dominance-based decision rules. Eur J Oper Res. 2007;181:1030–1044. [CrossRef]
  • [25] Abdellatif S, Hassine MAB, Yahia SB, Bouzeghoub A. ARCID: a new approach to deal with imbalanced datasets classification. In: International Conference on Current Trends in Theory and Practice of Informatics; 2018. p. 569–580. [CrossRef]
  • [26] Krawczyk B. Learning from imbalanced data: open challenges and future directions. Prog Artif Intell. 2016;5:221–232. [CrossRef]
  • [27] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357. [CrossRef]
  • [28] JAMM Software Tool. Available at: http://idss.cs.put.poznan.pl/site/75.html. Accessed April 12, 2021.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Klinik Kimya
Bölüm Research Articles
Yazarlar

Ahmet Topal 0000-0002-9185-3927

Nilgün Güler Bayazıt Bu kişi benim 0000-0003-0221-294X

Yasemen Uçan Bu kişi benim 0000-0001-7634-7869

Yayımlanma Tarihi 29 Aralık 2023
Gönderilme Tarihi 22 Kasım 2021
Yayımlandığı Sayı Yıl 2023 Cilt: 41 Sayı: 6

Kaynak Göster

Vancouver Topal A, Güler Bayazıt N, Uçan Y. Prediction of compressive strength class of concrete with dominance based rough set approach*. SIGMA. 2023;41(6):1088-95.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/