Evaluation of Dimension Stone According to Resistance to Freeze–Thaw Cycling to Use in Cold Regions

Document Type : Regular Article


1 Associate Professor, Department of Mining Engineering, Faculty of Environment, Urmia University of Technology, Urmia, Iran

2 Assistant Professor, Department of Mining Engineering, Faculty of Environment, Urmia University of Technology, Urmia, Iran

3 Ph.D. Candidate, Department of Civil Engineering, University of Calabria, 87036 Rende, Italy

4 Professor, Department of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

5 M.S., Department of Civil Engineering, Faculty of Environment, Urmia University of Technology, Urmia, Iran

6 Ph.D. Candidate, Department of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran

7 Professor, Department of Civil Engineering, Korea Maritime and Ocean University, Pusan 49112, Korea

8 Professor, College of IT Convergence, Gachon University, Seongnam 13120, Korea


Freezing is one of the most effective natural and environmental factors on the physical and mechanical characteristics of dimension stones. Since, freezing is a destructive agent, thus causes the undesirable stone conditions and reduces quality and its efficiency. This study, it was aimed to evaluate and rank the dimension stones according to their changes in physical and mechanical properties due to freezing conditions. For this purpose, 14 rock types of the most widely used dimension stones in cold regions were collected and transferred to the laboratory to determine their physical and mechanical characteristics. In laboratory tests, standard samples of stones were prepared, and for all of the samples Uniaxial Compressive Strength (UCS), Durability Index (DI), Density (D), and Water absorption percentage (Wa) were determined before and after different freezing–thawing cycles. Then utility degree of studied stones in frost condition was assessed using the preference ranking organization method for enrichment of evaluations (PROMETHEE) multi-criteria decision-making method. The results of the study showed that samples of A3 (Piranshahr Granat), A10 (Hamadan black granite), A8 (Azarshahr yellow travertine), and A4 (Mahabad gray granite) are in order from the highest degree of desirability in a condition of freezing–thawing and for use in cold climates are especially suitable for use in outdoor and urban spaces. In addition, the results of the laboratory were evaluated by the PSO algorithm for clustering analysis and com-pared with the ranking result by PROMETHEE. The results obtained demonstrated the proposed approach could be an efficient tool in the evaluation of the freezing phenomenon on physical and mechanical properties of dimension stones.


Main Subjects

[1]     Instanes A. Incorporating climate warming scenarios in coastal permafrost engineering design – Case studies from Svalbard and northwest Russia. Cold Reg Sci Technol 2016. https://doi.org/10.1016/j.coldregions.2016.09.004.
[2]     Tounsi H, Rouabhi A, Jahangir E, Guérin F. Mechanical behavior of frozen metapelite: Laboratory investigation and constitutive modeling. Cold Reg Sci Technol 2020. https://doi.org/10.1016/j.coldregions.2020.103058.
[3]     Zhang J, Fu H, Huang Z, Wu Y, Chen W, Shi Y. Experimental study on the tensile strength and failure characteristics of transversely isotropic rocks after freeze-thaw cycles. Cold Reg Sci Technol 2019. https://doi.org/10.1016/j.coldregions.2019.04.006.
[4]     Foruzanmehr A. Thermal comfort in hot dry climates: Traditional dwellings in Iran. 2017. https://doi.org/10.4324/9781315527130.
[5]     Farajzadeh H, Matzarakis A. Quantification of climate for tourism in the northwest of Iran. Meteorol Appl 2009. https://doi.org/10.1002/met.155.
[6]     Ma Q, Ma D, Yao Z. Influence of freeze-thaw cycles on dynamic compressive strength and energy distribution of soft rock specimen. Cold Reg Sci Technol 2018. https://doi.org/10.1016/j.coldregions.2018.04.014.
[7]     Zhou XP, Li CQ, Zhou LS. The effect of microstructural evolution on the permeability of sandstone under freeze-thaw cycles. Cold Reg Sci Technol 2020. https://doi.org/10.1016/j.coldregions.2020.103119.
[8]     Li J, Kaunda RB, Zhou K. Experimental investigations on the effects of ambient freeze-thaw cycling on dynamic properties and rock pore structure deterioration of sandstone. Cold Reg Sci Technol 2018. https://doi.org/10.1016/j.coldregions.2018.06.015.
[9]     Yang X, Jiang A, Li M. Experimental investigation of the time-dependent behavior of quartz sandstone and quartzite under the combined effects of chemical erosion and freeze–thaw cycles. Cold Reg Sci Technol 2019. https://doi.org/10.1016/j.coldregions.2019.03.008.
[10]   Matsuoka N. Mechanisms of rock breakdown by frost action: An experimental approach. Cold Reg Sci Technol 1990. https://doi.org/10.1016/S0165-232X(05)80005-9.
[11]   Nicholson DT, Nicholson FH. Physical deterioration of sedimentary rocks subjected to experimental freeze-thaw weathering. Earth Surf. Process. Landforms, 2000. https://doi.org/10.1002/1096-9837(200011)25:12<1295::AID-ESP138>3.0.CO;2-E.
[12]   Mutlutürk M, Altindag R, Türk G. A decay function model for the integrity loss of rock when subjected to recurrent cycles of freezing-thawing and heating-cooling. Int J Rock Mech Min Sci 2004. https://doi.org/10.1016/S1365-1609(03)00095-9.
[13]   Altindag R, Alyildiz IS, Onargan T. Mechanical property degradation of ignimbrite subjected to recurrent freeze-thaw cycles. Int J Rock Mech Min Sci 2004. https://doi.org/10.1016/j.ijrmms.2004.03.005.
[14]   Chen TC, Yeung MR, Mori N. Effect of water saturation on deterioration of welded tuff due to freeze-thaw action. Cold Reg Sci Technol 2004. https://doi.org/10.1016/j.coldregions.2003.10.001.
[15]   Karaca Z. Water absorption and dehydration of natural stones versus time. Constr Build Mater 2010. https://doi.org/10.1016/j.conbuildmat.2009.10.029.
[16]   Tan X, Chen Weizhong W, Yang J, Cao J. Laboratory investigations on the mechanical properties degradation of granite under freeze-thaw cycles. Cold Reg Sci Technol 2011. https://doi.org/10.1016/j.coldregions.2011.05.007.
[17]   Bayram F. Predicting mechanical strength loss of natural stones after freeze-thaw in cold regions. Cold Reg Sci Technol 2012. https://doi.org/10.1016/j.coldregions.2012.07.003.
[18]   Gökçe MV, Ince I, Fener M, Taşkiran T, Kayabali K. The effects of freeze-thaw (F-T) cycles on the Gödene travertine used in historical structures in Konya (Turkey). Cold Reg Sci Technol 2016. https://doi.org/10.1016/j.coldregions.2016.04.005.
[19]   Jamshidi A, Nikudel MR, Khamehchiyan M. Predicting the long-term durability of building stones against freeze-thaw using a decay function model. Cold Reg Sci Technol 2013. https://doi.org/10.1016/j.coldregions.2013.03.007.
[20]   Khanlari G, Sahamieh RZ, Abdilor Y. The effect of freeze–thaw cycles on physical and mechanical properties of Upper Red Formation sandstones, central part of Iran. Arab J Geosci 2015. https://doi.org/10.1007/s12517-014-1653-y.
[21]   Bell FG. Engineering properties of soils and rocks. 3rd ed. London: 1993.
[22]   Hori M, Morihiro H. Micromechanical analysis on deterioratIon due to freezing and thawing in porous brittle materials. Int J Eng Sci 1998. https://doi.org/10.1016/S0020-7225(97)00080-3.
[23]   Tuǧrul A. The effect of weathering on pore geometry and compressive strength of selected rock types from Turkey. Eng Geol 2004. https://doi.org/10.1016/j.enggeo.2004.05.008.
[24]   Jamshidi A, Nikudel MR, Khamehchiyan M. A novel physico-mechanical parameter for estimating the mechanical strength of travertines after a freeze–thaw test. Bull Eng Geol Environ 2017. https://doi.org/10.1007/s10064-016-0873-7.
[25]   Liping W, Ning L, Jilin Q, Yanzhe T, Shuanhai X. A study on the physical index change and triaxial compression test of intact hard rock subjected to freeze-thaw cycles. Cold Reg Sci Technol 2019. https://doi.org/10.1016/j.coldregions.2019.01.001.
[26]   Peng X, Yimin W, Zijian W, Le H. Distribution laws of freeze-thaw cycles and unsaturated concrete experiments in cold-region tunnels. Cold Reg Sci Technol 2020. https://doi.org/10.1016/j.coldregions.2019.102985.
[27]   Zhang S, Zhang J, Gui Y, Chen W, Dai Z. Deformation properties of coarse-grained sulfate saline soil under the freeze-thaw-precipitation cycle. Cold Reg Sci Technol 2020. https://doi.org/10.1016/j.coldregions.2020.103121.
[28]   Dağdeviren M. Decision making in equipment selection: An integrated approach with AHP and PROMETHEE. J Intell Manuf 2008. https://doi.org/10.1007/s10845-008-0091-7.
[29]   Athawale VM, Chakraborty S. Facility Location Selection using PROMETHEE II Method. Int Conf Ind Eng Oper Manag 2010.
[30]   Mikaeil R, Gharahasanlou Jafarnejad E, Aryafar A. Geotechnical Risks Assessment During the Second part of Emamzadeh Hashem (AS) Tunnel Using FDAHP-PROMETHEE. Amirkabir J Civ Eng 2018;49:247–50.
[31]   Ghadernejad S, Jafarpour A, Ahmadi P. Application of an integrated decision-making approach based on FDAHP and PROMETHEE for selection of optimal coal seam for mechanization; A case study of the Tazareh coal mine complex, Iran. Int J Min Geo-Engineering 2019. https://doi.org/10.22059/IJMGE.2018.255070.594727.
[32]   Iphar M, Alpay S. A mobile application based on multi-criteria decision-making methods for underground mining method selection. Int J Mining, Reclam Environ 2019. https://doi.org/10.1080/17480930.2018.1467655.
[33]   Sitorus F, Cilliers JJ, Brito-Parada PR. Multi-criteria decision making for the choice problem in mining and mineral processing: Applications and trends. Expert Syst Appl 2019. https://doi.org/10.1016/j.eswa.2018.12.001.
[34]   Mikaeil R, Gharahasanlou EJ, Jafarpour A. Ranking and Evaluating the Coal Seam Mechanization Based on Geological Conditions. Geotech Geol Eng 2020. https://doi.org/10.1007/s10706-020-01200-0.
[35]   Dayo-Olupona O, Genc B, Onifade M. Technology adoption in mining: A multi-criteria method to select emerging technology in surface mines. Resour Policy 2020. https://doi.org/10.1016/j.resourpol.2020.101879.
[36]   Dachowski R, Gałek K. Selection of the best method for underpinning foundations using the PROMETHEE II method. Sustain 2020. https://doi.org/10.3390/su12135373.
[37]   Prvulovic S, Dratolmac G, Radovanović LZ. Application of Promethee-Gaia methodology in the choice of systems for drying paltry-seeds and powder materials. Stroj Vestnik/Journal Mech Eng 2011. https://doi.org/10.5545/sv-jme.2008.068.
[38]   Bogdanovic D, Nikolic D, Ivana I. Mining method selection by integrated AHP and PROMETHEE method. An Acad Bras Cienc 2012. https://doi.org/10.1590/S0001-37652012005000013.
[39]   Abedi M, Ali Torabi S, Norouzi GH, Hamzeh M, Elyasi GR. PROMETHEE II: A knowledge-driven method for copper exploration. Comput Geosci 2012. https://doi.org/10.1016/j.cageo.2011.12.012.
[40]   Shirsikar SG, Patil S. Optimization of energy charges using improved PROMETHEE method. ISOR J Commun Eng 2013:36–42.
[41]   Balali V, Zahraie B, Roozbahani A. A Comparison of AHP and PROMETHEE Family Decision Making Methods for Selection of Building Structural System. Am J Civ Eng Archit 2014. https://doi.org/10.12691/ajcea-2-5-1.
[42]   Mikaeil R, Abdollahi Kamran M, Sadegheslam G, Ataei M. Ranking sawability of dimension stone using PROMETHEE method. J Min Environ (INTERNATIONAL J Min Environ ISSUES) 2015. https://doi.org/10.22044/jme.2015.477.
[43]   Mladineo M, Jajac N, Rogulj K. A simplified approach to the PROMETHEE method for priority setting in management of mine action projects. Croat Oper Res Rev 2016. https://doi.org/10.17535/crorr.2016.0017.
[44]   Balusa BC, Singam J. Underground Mining Method Selection Using WPM and PROMETHEE. J Inst Eng Ser D 2018. https://doi.org/10.1007/s40033-017-0137-0.
[45]   Brown ET. Rock characterization testing and monitoring. ISRM suggested methods. Rock Charact Test Monit ISRM Suggest Methods 1981. https://doi.org/10.1016/0148-9062(81)90524-6.
[46]   Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018. https://doi.org/10.1016/j.jobe.2018.01.007.
[47]   Naderpour H, Nagai K, Fakharian P, Haji M. Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Compos Struct 2019. https://doi.org/10.1016/j.compstruct.2019.02.048.
[48]   Dormishi A, Ataei M, Mikaeil R, Khalokakaei R, Haghshenas SS. Evaluation of gang saws’ performance in the carbonate rock cutting process using feasibility of intelligent approaches. Eng Sci Technol an Int J 2019. https://doi.org/10.1016/j.jestch.2019.01.007.
[49]   Shaffiee Haghshenas S, Mikaeil R, Abdollahi Kamran M, Shaffiee Haghshenas S, Hosseinzadeh Gharehgheshlagh H. Selecting the suitable tunnel supporting system using an integrated decision support system, (Case study: Dolaei tunnel of touyserkan, iran). J Soft Comput Civ Eng 2019. https://doi.org/10.22115/SCCE.2020.212995.1150.
[50]   Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng Sci Technol an Int J 2020. https://doi.org/10.1016/j.jestch.2019.05.013.
[51]   Ford E, Maneparambil K, Neithalath N. Machine learning on microstructural chemical maps to classify component phases in cement pastes. J Soft Comput Civ Eng 2021. https://doi.org/10.22115/SCCE.2021.302400.1357.
[52]   Saber A. Effects of window-to-wall ratio on energy consumption: Application of numerical and ann approaches. J Soft Comput Civ Eng 2021. https://doi.org/10.22115/SCCE.2021.281977.1299.
[53]   Shaffiee Haghshenas S, Shaffiee Haghshenas S, Abduelrhman MA, Shervin Z, Mikaeil R. Identifying and Ranking of Mechanized Tunneling Project’s Risks by Using A Fuzzy Multi-Criteria Decision Making Technique. J Soft Comput Civ Eng 2022;6:29–45.
[54]   Hosseini SM, Ataei M, Khalokakaei R, Mikaeil R, Haghshenas SS. Investigating the role of coolant and lubricant fluids on the performance of cutting disks (Case study: Hard rocks). Rud Geol Naft Zb 2019. https://doi.org/10.17794/rgn.2019.2.2.
[55]   Mikaeil R, Bakhshinezhad H, Haghshenas SS, Ataei M. Stability analysis of tunnel support systems using numerical and intelligent simulations (Case study: Kouhin tunnel of qazvin-rasht railway). Rud Geol Naft Zb 2019. https://doi.org/10.17794/rgn.2019.2.1.
[56]   Shirani Faradonbeh R, Shaffiee Haghshenas S, Taheri A, Mikaeil R. Application of self-organizing map and fuzzy c-mean techniques for rockburst clustering in deep underground projects. Neural Comput Appl 2020. https://doi.org/10.1007/s00521-019-04353-z.
[57]   Noori AM, Mikaeil R, Mokhtarian M, Haghshenas SS, Foroughi M. Feasibility of Intelligent Models for Prediction of Utilization Factor of TBM. Geotech Geol Eng 2020. https://doi.org/10.1007/s10706-020-01213-9.
[58]   Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Gallelli V, Astarita V. Development of a binary classification model to assess safety in transportation systems using GMDH-type neural network algorithm. Sustain 2020. https://doi.org/10.3390/SU12176735.
[59]   Morosini AF, Haghshenas SS, Haghshenas SS, Geem ZW. Development of a binary model for evaluating water distribution systems by a pressure driven analysis (PDA) approach. Appl Sci 2020. https://doi.org/10.3390/app10093029.
[60]   Kennedy J, Eberhart R. Proceedings of ICNN’95 - International Conference on Neural Networks. Part. Swarm Optim., 1995.
[61]   Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H. Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 2016. https://doi.org/10.1007/s00366-016-0447-0.
[62]   Shahnazar A, Nikafshan Rad H, Hasanipanah M, Tahir MM, Jahed Armaghani D, Ghoroqi M. A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ Earth Sci 2017. https://doi.org/10.1007/s12665-017-6864-6.
[63]   Mikaeil R, Shaffiee Haghshenas S, Sedaghati Z. Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: the second part of Emamzade Hashem tunnel). Nat Hazards 2019. https://doi.org/10.1007/s11069-019-03688-z.
[64]   Armaghani DJ, Mohamad ET, Narayanasamy MS, Narita N, Yagiz S. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Sp Technol 2017. https://doi.org/10.1016/j.tust.2016.12.009.
[65]   Yagiz S, Ghasemi E, Adoko AC. Prediction of Rock Brittleness Using Genetic Algorithm and Particle Swarm Optimization Techniques. Geotech Geol Eng 2018. https://doi.org/10.1007/s10706-018-0570-3.
[66]   Lloyd SP. Least Squares Quantization in PCM. IEEE Trans Inf Theory 1982. https://doi.org/10.1109/TIT.1982.1056489.
[67]   Aryafar A, Mikaeil R, Haghshenas SS, Haghshenas SS. Application of metaheuristic algorithms to optimal clustering of sawing machine vibration. Meas J Int Meas Confed 2018. https://doi.org/10.1016/j.measurement.2018.03.056.
[68]   Mikaeil R, Haghshenas SS, Hoseinie SH. Rock Penetrability Classification Using Artificial Bee Colony (ABC) Algorithm and Self-Organizing Map. Geotech Geol Eng 2018. https://doi.org/10.1007/s10706-017-0394-6.
[69]   Salemi A, Mikaeil R, Haghshenas SS. Integration of Finite Difference Method and Genetic Algorithm to Seismic analysis of Circular Shallow Tunnels (Case Study: Tabriz Urban Railway Tunnels). KSCE J Civ Eng 2018. https://doi.org/10.1007/s12205-017-2039-y.
[70]   Aryafar A, Mikaeil R, Shafiee Haghshenas S, shafiei haghshenas  sami. Utilization of Soft Computing for Evaluating the Performance of Stone Sawing Machines, Iranian Quarries. Int J Min Geo-Engineering 2018.
[71]   Mikaeil R, Beigmohammadi M, Bakhtavar E, Haghshenas SS. Assessment of risks of tunneling project in Iran using artificial bee colony algorithm. SN Appl Sci 2019. https://doi.org/10.1007/s42452-019-1749-9.
[72]   Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Astarita V, Haghshenas AS. Feasibility of stochastic models for evaluation of potential factors for safety: A case study in southern Italy. Sustain 2020. https://doi.org/10.3390/su12187541.