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Year 2020, Volume: 7 Issue: 2, 99 - 108, 26.06.2020
https://doi.org/10.17350/HJSE19030000178

Abstract

References

  • 1. Arıoğlu E, Köylüoğlu ÖS. Discussion of “estimation of coarse aggregate strength in high strength concrete” by TP Chang and N K Su. ACI Materials Journal November-December (1996) 637-639.
  • 2. Olson DL. Comparison of weights in TOPSIS models. Mathematical and Computer Modelling 40 (2004) 721-727.
  • 3. Wu DS, Olson DL. A TOPSIS data mining demonstration and application to credit scoring. International Journal of Data Warehousing and Mining 2(3) (2006) 1-10.
  • 4. Huanga IB, Keislerb J, Linkov I. Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of The Total Environment 409(19) (2011) 3578–3594.
  • 5. Velasquez M, Hester PT. An analysis of multi-criteria decision making methods. International Journal of Operations Research 10(2) (2013) 56-66.
  • 6. Feng CM, Wang RT. Considering the financial ratios on the performance evaluation of highway bus industry. Transport Reviews: A Transnational Transdisciplinary Journal, 21(4) (2001) 449-467.
  • 7. Jahan A, Ismail MY, Sapuan, SM, Mustapha F. Material screening and choosing methods–a review. Materials & Design 31 (2010) 696- 705.
  • 8. Warren Liao T. Two interval type 2 fuzzy TOPSIS material selection methods. Materials & Design (2015) 1088-1099.
  • 9. Yazdani M, Payam AF. A comparative study on material selection of microelectromechanical systems electrostatic actuators using Ashby, VIKOR and TOPSIS. Materials & Design 65 (2015) 328-334.
  • 10. Kaspar J, Baehre D, Vielhaber M. Material selection based on a product and production engineering integration framework. Procedia CIRP 50 (2016) 2–7.
  • 11. Mousavi-Nasab SH, Sotoudeh-Anvari A. A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design 121 (2017) 237-253.
  • 12. Zhang K, Zhan J, Yao Y. TOPSIS method based on a fuzzy covering approximation space: An application to biological nano-materials selection. Information Sciences 502 (2019) 297-329.
  • 13. Maghsoodi AI, Maghsoodi AI, Poursoltan P, Antucheviciene J, Turskis Z. Dam construction material selection by implementing the integrated SWARA–CODAS approach with target-based attributes. Archives of Civil and Mechanical Engineering 19(4) (2019) 1194-1210.
  • 14. Papathanasiou J, Ploskas N, Bournaris T, Manos B. A Decision Support System for Multiple Criteria Alternative Ranking Using TOPSIS and VIKOR: A Case Study on Social Sustainability in Agriculture. In: Liu S., Delibašić B., Oderanti F. (eds) Decision Support Systems VI - Addressing Sustainability and Societal Challenges. ICDSST 2016. Lecture Notes in Business InformationProcessing, 250. Springer, Cham, 2016.
  • 15. Ploskas N, Papathanasiou J. A decision support system for multiple criteria alternative ranking using TOPSIS and VIKOR in fuzzy and nonfuzzy environments. Fuzzy Sets and Systems Available online 23 January 2019, in press (2019).
  • 16. Kwok PK, Lau HYK. Hotel selection using a modified TOPSISbased decision support algorithm. Decision Support Systems 120 (2019) 95-105.
  • 17. Konstantinos I, Georgios T, Garyfalos A. A Decision Support System methodology for selecting wind farm installation locations using AHP and TOPSIS: Case study in Eastern Macedonia and Thrace region, Greece. Energy Policy 132 (2019) 232-246.
  • 18. TS EN 933-9+A1. Tests for geometrical properties of aggregates - part 9: assessment of fines - methylene blue test. Turkish Standardization Institute, Ankara (in Turkish), 2014.
  • 19. TS EN 196-1. Methods of testing cement - part 1: determination of strength. Turkish Standardization Institute Ankara (in Turkish), 2009.
  • 20. Postacıoğlu B. Concrete-Binding Materials. Aggregates Concrete Volume 2, Turkey, Istanbul Technical University (in Turkish), 1986.

A Knowledge-Based System for Fine Aggregate Material Problem Selection in Concrete Production

Year 2020, Volume: 7 Issue: 2, 99 - 108, 26.06.2020
https://doi.org/10.17350/HJSE19030000178

Abstract

One of the main problems of our country is inability to select the right materials of high quality in production. Decision making based on multiple criteria has an important role to do the right selections in each sector. One of these sectors is construction. Construction sector develops rapidly and using the right material is an important issue. Using the right material in this period when construction sector develops rapidly has a great importance. In the construction sector, the building material which has been used the most widely from past to present is concrete. In this study, a knowledge-based system via TOPSIS approach was proposed to generalize the multi-criteria decision making problems of fine aggregate material selection in concrete production. In addition, six different mortar series were produced by using the fine aggregates which were obtained from various plants used in the production of ready-mixed concrete in Kütahya and CEN Standard sand. The methylene blue, physical and mechanical tests were carried out on the produced mortars in order to get an idea for the strength and durability of concrete. The purpose of the study was to determine which of the five different fine aggregates had characteristics that are the closest to those of CEN Standard sand based on defined these multi criteria. It was found that the best fine aggregate series was A based on the defined criteria by considering the results of the experiments, assigning weights based on importance and analyzing these with TOPSIS approach.

References

  • 1. Arıoğlu E, Köylüoğlu ÖS. Discussion of “estimation of coarse aggregate strength in high strength concrete” by TP Chang and N K Su. ACI Materials Journal November-December (1996) 637-639.
  • 2. Olson DL. Comparison of weights in TOPSIS models. Mathematical and Computer Modelling 40 (2004) 721-727.
  • 3. Wu DS, Olson DL. A TOPSIS data mining demonstration and application to credit scoring. International Journal of Data Warehousing and Mining 2(3) (2006) 1-10.
  • 4. Huanga IB, Keislerb J, Linkov I. Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of The Total Environment 409(19) (2011) 3578–3594.
  • 5. Velasquez M, Hester PT. An analysis of multi-criteria decision making methods. International Journal of Operations Research 10(2) (2013) 56-66.
  • 6. Feng CM, Wang RT. Considering the financial ratios on the performance evaluation of highway bus industry. Transport Reviews: A Transnational Transdisciplinary Journal, 21(4) (2001) 449-467.
  • 7. Jahan A, Ismail MY, Sapuan, SM, Mustapha F. Material screening and choosing methods–a review. Materials & Design 31 (2010) 696- 705.
  • 8. Warren Liao T. Two interval type 2 fuzzy TOPSIS material selection methods. Materials & Design (2015) 1088-1099.
  • 9. Yazdani M, Payam AF. A comparative study on material selection of microelectromechanical systems electrostatic actuators using Ashby, VIKOR and TOPSIS. Materials & Design 65 (2015) 328-334.
  • 10. Kaspar J, Baehre D, Vielhaber M. Material selection based on a product and production engineering integration framework. Procedia CIRP 50 (2016) 2–7.
  • 11. Mousavi-Nasab SH, Sotoudeh-Anvari A. A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design 121 (2017) 237-253.
  • 12. Zhang K, Zhan J, Yao Y. TOPSIS method based on a fuzzy covering approximation space: An application to biological nano-materials selection. Information Sciences 502 (2019) 297-329.
  • 13. Maghsoodi AI, Maghsoodi AI, Poursoltan P, Antucheviciene J, Turskis Z. Dam construction material selection by implementing the integrated SWARA–CODAS approach with target-based attributes. Archives of Civil and Mechanical Engineering 19(4) (2019) 1194-1210.
  • 14. Papathanasiou J, Ploskas N, Bournaris T, Manos B. A Decision Support System for Multiple Criteria Alternative Ranking Using TOPSIS and VIKOR: A Case Study on Social Sustainability in Agriculture. In: Liu S., Delibašić B., Oderanti F. (eds) Decision Support Systems VI - Addressing Sustainability and Societal Challenges. ICDSST 2016. Lecture Notes in Business InformationProcessing, 250. Springer, Cham, 2016.
  • 15. Ploskas N, Papathanasiou J. A decision support system for multiple criteria alternative ranking using TOPSIS and VIKOR in fuzzy and nonfuzzy environments. Fuzzy Sets and Systems Available online 23 January 2019, in press (2019).
  • 16. Kwok PK, Lau HYK. Hotel selection using a modified TOPSISbased decision support algorithm. Decision Support Systems 120 (2019) 95-105.
  • 17. Konstantinos I, Georgios T, Garyfalos A. A Decision Support System methodology for selecting wind farm installation locations using AHP and TOPSIS: Case study in Eastern Macedonia and Thrace region, Greece. Energy Policy 132 (2019) 232-246.
  • 18. TS EN 933-9+A1. Tests for geometrical properties of aggregates - part 9: assessment of fines - methylene blue test. Turkish Standardization Institute, Ankara (in Turkish), 2014.
  • 19. TS EN 196-1. Methods of testing cement - part 1: determination of strength. Turkish Standardization Institute Ankara (in Turkish), 2009.
  • 20. Postacıoğlu B. Concrete-Binding Materials. Aggregates Concrete Volume 2, Turkey, Istanbul Technical University (in Turkish), 1986.
There are 20 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Abdullah Demir This is me

Publication Date June 26, 2020
Published in Issue Year 2020 Volume: 7 Issue: 2

Cite

Vancouver Demir A. A Knowledge-Based System for Fine Aggregate Material Problem Selection in Concrete Production. Hittite J Sci Eng. 2020;7(2):99-108.

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