Selecting Appropriate Dimension Stone in Processing Plants using the Fuzzy Delphi Analytic Hierarchy Process

Akbar Esmaeilzadeh; Masoud Akhyani; Yavar Jalili Kashtiban; Reza Mikaeil; Raffaele Zinno; Sina Shaffiee Haghshenas

doi:10.31181/sems31202553e

Authors

Akbar Esmaeilzadeh Department of Mining Engineering, Faculty of Mining industries and Technologies, Urmia University of Technology, Urmia, Iran Author https://orcid.org/0000-0002-8633-9683
Masoud Akhyani Faculty of Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran Author https://orcid.org/0009-0003-7736-4988
Yavar Jalili Kashtiban Ferrovial Construction Canada Inc., Canada Author https://orcid.org/0000-0002-2262-8961
Reza Mikaeil Department of Mining Engineering, Faculty of Mining Industries and Technologies, Urmia University of Technology, Urmia, Iran Author https://orcid.org/0000-0001-8404-3216
Raffaele Zinno Department of Environmental Engineering, University of Calabria, 87036 Rende, Italy Author https://orcid.org/0000-0003-1533-6968
Sina Shaffiee Haghshenas Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy Author https://orcid.org/0000-0003-2859-3920

DOI:

https://doi.org/10.31181/sems31202553e

Keywords:

Dimension Stones, Granite, Stone Processing Plants, Analytic Hierarchy Process, Delphi Method, Fuzzy Sets, Engineering Aspects

Abstract

Selecting the most appropriate dimension stones has always been one of the vital issues in processing plants which stone is processing. This study aims to develop a new hierarchical model to evaluate and rank the dimension stone with the use of effective and major criteria such as beauty and durability, stone slab ability, supply chain, profitability, domestic and foreign market demand, and environmental factors and simultaneously, considering subjective judgments of decision-makers. The proposed approach is based on the fuzzy Delphi analytic hierarchy process method to calculate the effectiveness of the criteria and rank the dimension stone by decision-makers. This model is utilized for some stone process plants to assess and rank some famous Iranian granite. Then, the most appropriate stones for stone processing process plants are ranked. The ranking results showed that Chayan black granite with a 0.24 value, is the best stone to sawing in studied stone process plants. Finally, it was deduced that the most appropriate selection of dimension stone can reliably be done using the developed approach according to the most important effective criteria.

References

Winkler, E. M. (1975). Properties of Stone. In: Stone: Properties, Durability in Man’s Environment, Applied Mineralogy, pp. 27-50. Springer, Vienna. https://doi.org/10.1007/978-3-7091-3819-9_2.

Careddu, N., & Marras, G. (2015). Marble processing for future uses of CaCO3-microfine dust: a study on wearing out of tools and consumable materials in stoneworking factories. Mineral Processing and Extractive Metallurgy Review, 36(3), 183-191. https://doi.org/10.1080/08827508.2014.900616.

Careddu, N., & Siotto, G. (2011). Promoting ecological sustainable planning for natural stone quarrying. The case of the Orosei Marble Producing Area in Eastern Sardinia. Resources Policy, 36(4), 304-314. https://doi.org/10.1016/j.resourpol.2011.07.002.

Ashmole, I., & Motloung, M. (2008). Dimension Stone: The Latest Trends in Exploration and Production Technology. In: Proceedings of the International Conference on Surface Mining, Johannesburg, South Africa, 5-8 August 2008.

Tahernejad, M. M., Ataei, M., & Khalokakaei, R. (2013). A strategic analysis of Iran’s dimensional stone mines using SWOT method. Arabian Journal for Science and Engineering, 38, 149-154. https://doi.org/10.1007/s13369-012-0422-z.

Wei, X., Wang, C. Y., & Zhou, Z. H. (2003). Study on the fuzzy ranking of granite sawability. Journal of Materials Processing Technology, 139(1-3), 277-280. https://doi.org/10.1016/S0924-0136(03)00235-8.

Kahraman, S., Ulker, U., & Delibalta, M. (2007). A quality classification of building stones from P-wave velocity and its application to stone cutting with gang saws. Journal of the Southern African Institute of Mining and Metallurgy, 107(7), 427-430.

Tutmez, B., Kahraman, S., & Gunaydin, O. (2007). Multifactorial fuzzy approach to the sawability classification of building stones. Construction and Building Materials, 21(8), 1672-1679. https://doi.org/10.1016/j.conbuildmat.2006.05.023.

Mikaeil, R., Yousefi, R., Ataei, M., & Abasian Farani, R. (2011). Development of a new classification system for assessing of carbonate rock sawability. Archives of Mining Sciences, 56(1), 59-70.

Mikaeil, R., Yousefi, R., & Ataei, M. (2011). Sawability ranking of carbonate rock using fuzzy analytical hierarchy process and TOPSIS approaches. Scientia Iranica, 18(5), 1106-1115. https://doi.org/10.1016/j.scient.2011.09.009.

Ataei, M., Mikaeil, R., Hoseinie, S. H., & Hosseini, S. M. (2012). Fuzzy analytical hierarchy process approach for ranking the sawability of carbonate rock. International Journal of Rock Mechanics and Mining Sciences, 50, 83-93. https://doi.org/10.1016/j.ijrmms.2011.12.002.

Sadegheslam, G., Mikaeil, R., Rooki, R., Ghadernejad, S., & Ataei, M. (2013). Predicting the production rate of diamond wire saws using multiple nonlinear regression analysis. Geosystem Engineering, 16(4), 275-285. https://doi.org/10.1080/12269328.2013.856276.

Mikaeil, R., Ozcelik, Y., Yousefi, R., Ataei, M., & Hosseini, S. M. (2013). Ranking the sawability of ornamental stone using Fuzzy Delphi and multi-criteria decision-making techniques. International Journal of Rock Mechanics and Mining Sciences, 58, 118-126. https://doi.org/10.1016/j.ijrmms.2012.09.002.

Dewangan, S., & Chattopadhyaya, S. (2014). Discussion on ranking the sawability of rocks using a combined multiple attribute decision making method. Applied Mechanics and Materials, 592, 864-868. https://doi.org/10.4028/www.scientific.net/AMM.592-594.864.

Mikaeil, R., Abdollahi, K. M., Sadegheslam, G., & Ataei, M. (2015). Ranking sawability of dimension stone using PROMETHEE method. Journal of Mining & Environment, 6(2), 263-271.

Hosseini, S. M., Ataei, M., Khalokakaei, R., Mikaeil, R., & Haghshenas, S. S. (2020). Study of the effect of the cooling and lubricant fluid on the cutting performance of dimension stone through artificial intelligence models. Engineering Science and Technology, an International Journal, 23(1), 71-81. https://doi.org/10.1016/j.jestch.2019.04.012.

Afradi, A., Alavi, I., & Moslemi, M. (2021). Selecting the most suitable method for extracting construction materials in Iran through the fuzzy multi-attribute decision-making model. Journal of the Institution of Engineers (India): Series D, 102, 113-123. https://doi.org/10.1007/s40033-020-00239-w.

Mikaeil, R., Esmaeilzade, A., & Shaffiee Haghshenas, S. (2021). Investigation of the relationship between schimazek's f-abrasiveness factor and current consumption in rock cutting process. Journal of Civil Engineering and Materials Application, 5(2), 47-55.

Shaffiee Haghshenas, S., Mikaeil, R., Esmaeilzadeh, A., Careddu, N., & Ataei, M. (2022). Statistical study to evaluate performance of cutting machine in dimension stone cutting process. Journal of Mining and Environment, 13(1), 53-67.

Hayati, M., Mahdevari, S., & Barani, K. (2023). An improved MADM-based SWOT analysis for strategic planning in dimension stones industry. Resources Policy, 80, 103287. https://doi.org/10.1016/j.resourpol.2022.103287.

Bagloo, H., & Ataee-pour, M. (2024). A Classification Model of Dimensional Stones Using AHP and Fuzzy Logic. Geotechnical and Geological Engineering, 42(2), 1173-1187. https://doi.org/10.1007/s10706-023-02611-5.

Guido, G., Haghshenas, S. S., Haghshenas, S. S., Vitale, A., Gallelli, V., & Astarita, V. (2022). Prioritizing the potential smartification measures by using an Integrated decision support system with sustainable development goals (a case study in southern Italy). Safety, 8(2), 35. https://doi.org/10.3390/safety8020035.

Ghoushchi, S. J., Haghshenas, S. S., Vahabzadeh, S., Guido, G., & Geem, Z. W. (2024). An integrated MCDM approach for enhancing efficiency in connected autonomous vehicles through augmented intelligence and IoT integration. Results in Engineering, 23, 102626. https://doi.org/10.1016/j.rineng.2024.102626.

Rad, M. Y., Haghshenas, S. S., Kanafi, P. R., & Haghshenas, S. S. (2012). Analysis of Protection of Body Slope in the Rockfill Reservoir Dams on the Basis of Fuzzy Logic. In: Proceedings of the IJCCI, 4th International Joint Conference on Computational Intelligence, Barcelona, Spain, 5-7 October 2012, pp. 367-373.

Shaffiee Haghshenas, S., Mikaeil, R., Abdollahi Kamran, M., Shaffiee Haghshenas, S., & Hosseinzadeh Gharehgheshlagh, H. (2019). Selecting the suitable tunnel supporting system using an integrated decision support system (Case study: Dolaei Tunnel of Touyserkan, Iran). Journal of Soft Computing in Civil Engineering, 3(4), 51-66. https://doi.org/10.22115/scce.2020.212995.1150.

Shaffiee Haghshenas, S., Shaffiee Haghshenas, S., Abduelrhman, M. A., Zare, S., & Mikaeil, R. (2022). Identifying and Ranking of Mechanized Tunneling Project's Risks by Using A Fuzzy Multi-Criteria Decision Making Technique. Journal of Soft Computing in Civil Engineering, 6(1), 29-45. https://doi.org/10.22115/scce.2022.305718.1366.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98. https://doi.org/10.1504/IJSSCI.2008.017590.

Liu, Y. C., & Chen, C. S. (2007). A new approach for application of rock mass classification on rock slope stability assessment. Engineering Geology, 89(1-2), 129-143. https://doi.org/10.1016/j.enggeo.2006.09.017.

Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. Elsevier Science Inc.