Quantitative Characterization of Marble Natural Aging through Pore Structure Image Analysis
Publication: Journal of Materials in Civil Engineering
Volume 35, Issue 9
Abstract
The goal of this study is the quantitative characterization of the degree of natural alteration of marble samples by using image analysis for the automatic characterization and comparison of the pore structure of rock samples before and after weathering. The proposed methodology is based on a pore exploration path-finding algorithm for the identification of paths developing within the porous domain of marble samples in both natural conditions and after weathering. Along each identified path, the pore radius is measured, providing a thorough description of the pore space statistical distribution. The path-finding approach was developed and applied to binarized images obtained from two-dimensional (2D) thin sections of marble samples in both natural conditions and after 10 years of natural decay. The results are expressed in terms of 2D porosity and statistical distributions of the pore radius of the samples preweathering and postweathering. A comparison with the information obtained from standardized laboratory tests used for the physical and mechanical characterization of stone material is also provided. From a computational point of view, the presented approach is highly parallelizable. The presented approach works wells in complex porous structures characterized by high path tortuosity, pore-size heterogeneity, and pore surface roughness. Moreover, the methodology is less affected by small-scale pore features and noise produced during image binarization, compared with other algorithms for pore structure morphological analysis such as skeleton-based and maximal ball approaches.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, such as pore throat calculation script.
References
Alqahtani, N., F. Alzubaidi, R. T. Armstrong, P. Swietojanski, and P. Mostaghimi. 2020. “Machine learning for predicting properties of porous media from 2D X-ray images.” J. Pet. Sci. Eng. 184 (Jan): 106514. https://doi.org/10.1016/j.petrol.2019.106514.
Al-Raoush, R. I., and I. T. Madhoun. 2017. “TORT3D: A MATLAB code to compute geometric tortuosity from 3D images of unconsolidated porous media.” Powder Technol. 320 (Oct): 99–107. https://doi.org/10.1016/j.powtec.2017.06.066.
Aral, İ. F., R. Boy, and A. R. Dinçer. 2021. “Effects of freeze-thawing cycles on the physical and mechanical properties of basaltic and dolomitic rocks evaluated with a decay function model.” Bull. Eng. Geol. Environ. 80 (4): 2955–2962. https://doi.org/10.1007/s10064-021-02132-6.
Arand, F., and J. Hesser. 2017. “Accurate and efficient ball algorithm for pore network extraction.” Comput. Geosci. 101 (Apr): 28–37. https://doi.org/10.1016/j.cageo.2017.01.004.
Buckman, J., S. A. Bankole, S. Zihms, H. Lewis, G. Couple, and P. W. M. Corbett. 2017. “Quantifying porosity through automated image collection and batch image processing: Case study of three carbonates and an aragonite cemented sandstone.” Geosciences 7 (3): 70. https://doi.org/10.3390/geosciences7030070.
Cai, J., Z. Zhang, W. Wei, D. Guo, S. Li, and P. Zhao. 2019. “The critical factors for permeability-formation factor relation in reservoir rocks: Pore-throat ratio, tortuosity and connectivity.” Energy 188 (Dec): 116051. https://doi.org/10.1016/j.energy.2019.116051.
Çelik, M. Y., and M. Sert. 2021. “An assessment of capillary water absorption changes related to the different salt solutions and their concentrations ratios in the Döğer tuff (Afyonkarahisar-Turkey) used as building stone of cultural heritages.” J. Build. Eng. 35 (Mar): 102102. https://doi.org/10.1016/j.jobe.2020.102102.
CEN (European Committee for Standardization). 2005. Natural stone test methods—Determination of sound speed propagation CEN committee. EN 14579. Brussels, Belgium: CEN.
CEN (European Committee for Standardization). 2006. Natural stone test methods. Determination of real density and apparent density, and of total and open porosity. EN 1936. Brussels, Belgium: CEN.
CEN (European Committee for Standardization). 2008. Natural stone test methods—Determination of water absorption at atmospheric pressure CEN Committee. EN 13755. Brussels, Belgium: CEN.
CEN (European Committee for Standardization). 2022. Natural stone test methods—Determination of flexural strength under concentrated load CEN committee. EN 12372. Brussels, Belgium: CEN.
Cnudde, V., A. Cwirzen, B. Masschaele, and P. J. S. Jacobs. 2009. “Porosity and microstructure characterization of building stones and concretes.” Eng. Geol. 103 (3–4): 76–83. https://doi.org/10.1016/j.enggeo.2008.06.014.
Da Fonseca, B. S., A. F. Pinto, A. Rodrigues, S. Piçarra, and M. F. Montemor. 2021. “The role of properties on the decay susceptibility and conservation issues of soft limestones: Contribution of Ançã Stone (Portugal).” J. Build. Eng. 44 (Dec): 102997. https://doi.org/10.1016/j.jobe.2021.102997.
Datta, D., N. Thakur, S. Ghosh, R. Poddar, and S. Sengupta. 2016. “Determination of porosity of rock samples from photomicrographs using image analysis.” In Proc., IEEE 6th Int. Conf. on Advanced Computing (IACC), 320–325. New York: IEEE.
Du Plessis, A., B. J. Olawuyi, W. P. Boshoff, and S. G. Le Roux. 2016. “Simple and fast porosity analysis of concrete using X-ray computed tomography.” Mater. Struct. 49 (1): 553–562. https://doi.org/10.1617/s11527-014-0519-9.
Ferrero, A. M., M. Migliazza, A. Spagnoli, and M. Zucali. 2014. “Micromechanics of intergranular cracking due to anisotropic thermal expansion in calcite marbles.” Eng. Fract. Mech. 130 (Nov): 42–52. https://doi.org/10.1016/j.engfracmech.2014.01.004.
Franzen, C., and P. W. Mirwald. 2004. “Moisture content of natural stone: Static and dynamic equilibrium with atmospheric humidity.” Environ. Geol. 46 (Aug): 391–401. https://doi.org/10.1007/s00254-004-1040-1.
Gostick, J. T. 2017. “Versatile and efficient pore network extraction method using marker-based watershed segmentation.” Phys. Rev. 96 (2): 023307. https://doi.org/10.1103/PhysRevE.96.023307.
Grelk, B., C. Christiansen, B. Schouenborg, and K. Malaga. 2007. “Durability of marble cladding—A comprehensive literature.” J. ASTM Int. 4 (1499): 105. https://doi.org/10.1520/JAI100857.
Hart, P. E., N. J. Nilsson, and B. Raphael. 1968. “A formal basis for the heuristic determination of minimum cost paths.” Syst. Sci. Cybern. 4 (2): 100–107. https://doi.org/10.1109/TSSC.1968.300136.
Ju, M., X. Li, X. Li, and G. Zhang. 2022. “A review of the effects of weak interfaces on crack propagation in rock: From phenomenon to mechanism.” Eng. Fract. Mech. 3 (Feb): 108297. https://doi.org/10.1016/j.engfracmech.2022.108297.
Liang, Y., P. Hu, S. Wang, S. Song, and S. Jiang. 2019. “Medial axis extraction algorithm specializing in porous media.” Powder Technol. 343 (Feb): 512–520. https://doi.org/10.1016/j.powtec.2018.11.061.
Lindquist, W. B., S. M. Lee, D. A. Coker, K. W. Jones, and P. Spanne. 1996. “Medial axis analysis of void structure in three-dimensional tomographic images of porous media.” J. Geophys. Res. Solid Earth 101 (B4): 8297–8310. https://doi.org/10.1029/95JB03039.
Liu, J., G. G. Pereira, and K. Regenauer-Lieb. 2014. “From characterisation of pore-structures to simulations of pore-scale fluid flow and the upscaling of permeability using microtomography: A case study of heterogeneous carbonates.” J. Geochem. Explor. 144 (Sep): 84–96. https://doi.org/10.1016/j.gexplo.2014.01.021.
Marini, P., and R. Bellopede. 2009. “Bowing of marble slabs: Evolution and correlation with mechanical decay.” Constr. Build. Mater. 23 (7): 2599–2605. https://doi.org/10.1016/j.conbuildmat.2009.02.010.
Mauk, M. G., R. Chiou, V. Genis, and E. Carr. 2013. “Image analysis of microfluidics: Visualization of flow at the microscale.” In Proc., ASEE Annual Conf. and Exposition. Washington, DC: American Society For Engineering and Education.
Murru, A., D. M. Freire-Lista, R. Fort, M. J. Varas-Muriel, and P. Meloni. 2018. “Evaluation of post-thermal shock effects in Carrara marble and Santa Caterina di Pittinuri limestone.” Constr. Build. Mater. 186 (Oct): 1200–1211. https://doi.org/10.1016/j.conbuildmat.2018.08.034.
Nicholson, D. T. 2001. “Pore properties as indicators of breakdown mechanisms in experimentally weathered limestones.” Earth Surf. Processes Landforms 26 (8): 819–838. https://doi.org/10.1002/esp.228.
Nilsson, N. J. 2014. Principles of artificial intelligence. Burlington, MA: Morgan Kaugfmann.
Øren, P. E., and S. Bakke. 2003. “Reconstruction of berea sandstone and pore-scale modeling of wettability effects.” J. Pet. Sci. Eng. 39 (3–4): 177–199. https://doi.org/10.1016/S0920-4105(03)00062-7.
Ozcelik, Y., and A. Ozguven. 2014. “Water absorption and drying features of different natural building stones.” Constr. Build. Mater. 63 (Jul): 257–270. https://doi.org/10.1016/j.conbuildmat.2014.04.030.
Pal, A. K., S. Garia, K. Ravi, and A. M. Nair. 2022. “Pore scale image analysis for petrophysical modeling.” Micron 154 (Mar): 103195. https://doi.org/10.1016/j.micron.2021.103195.
Rabbani, A., M. Babaei, R. Shams, Y. D. Wang, and T. Chung. 2020. “DeePore: A deep learning workflow for rapid and comprehensive characterization of porous materials.” Adv. Water Resour. 146 (Dec): 103787. https://doi.org/10.1016/j.advwatres.2020.103787.
Rabbani, A., S. Jamshid, and S. Salehi. 2014. “An automated simple algorithm for realistic pore network extraction from micro-tomography images.” J. Pet. Sci. Eng. 123 (Nov): 164–171. https://doi.org/10.1016/j.petrol.2014.08.020.
Russell, S. J., and P. Norvig. 2018. Artificial intelligence a modern approach. Boston: Pearson.
Salina Borello, E., C. Peter, F. Panini, and D. Viberti. 2022. “Application of algorithm for microstructure and transport properties characterization from 3D rock images.” Energy 239 (Jan): 122151. https://doi.org/10.1016/j.energy.2021.122151.
Schouenborg, B., B. Grelk, and K. Malaga. 2007. Testing and assessment of marble and limestone (TEAM)—Important results from a large European research project on cladding panels. West Conshohocken, PA: ASTM International.
Scrivano, S., L. Gaggero, and J. G. Aguilar. 2018. “Micro-porosity and minero-petrographic features influences on decay: Experimental data from four dimension stones.” Constr. Build. Mater. 173 (Jun): 342–349. https://doi.org/10.1016/j.conbuildmat.2018.04.041.
Shaked, D., and A. M. Bruckstein. 1998. “Pruning medial axes.” Comput. Vis. Image Understanding 69 (2): 156–169. https://doi.org/10.1006/cviu.1997.0598.
Sheppard, A. P., R. M. Sok, and H. Averdunk. 2004. “Techniques for image enhancement and segmentation of tomographic images of porous materials.” Phys. A: Stat. Mech. Appl. 339 (1): 145–151. https://doi.org/10.1016/j.physa.2004.03.057.
Shorten, C., and T. M. Khoshgoftaar. 2019. “A survey on image data augmentation for deep learning.” J. Big Data 6 (1): 1–48. https://doi.org/10.1186/s40537-019-0197-0.
Sousa, L., J. Menningen, R. López-Doncel, and S. Siegesmund. 2021. “Petrophysical properties of limestones: Influence on behaviour under different environmental conditions and applications.” Environ. Earth Sci. 80 (24): 814. https://doi.org/10.1007/s12665-021-10064-3.
UNI (Italian National Standards). 2011. Beni culturali - Materiali lapidei naturali ed artificiali - Misura della capacità di assorbimento di acqua mediante spugna di contatto. UNI 11432: 2011. Milano, Italy: UNI.
Viberti, D., C. Peter, E. S. Borello, and F. Panini. 2020. “Pore structure characterization through path-finding and Lattice Boltzmann simulation.” Adv. Water Resour. 141 (Jul): 103609. https://doi.org/10.1016/j.advwatres.2020.103609.
Wang, C., K. Wu, G. Scott, A. Akisanya, Q. Gan, and Y. Zhou. 2020. “A new method for pore structure quantification and pore network extraction from SEM images.” Energy Fuels 34 (1): 82–94. https://doi.org/10.1021/acs.energyfuels.9b02522.
Weiss, T., S. Siegesmund, and P. N. J. Rasolofosaon. 2000. “The relationship between deterioration, fabric, velocity and porosity constraint.” In Proc. of the 9th Int. Congress on Deterioration and Conservation of Stone, 215–223. Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/B978-044450517-0/50103-3.
Winkler, E. M. 1985. “A durability index for stone.” In Vol. 5 of Congrès international sur l'altération et la conservation de la pierre, 151–156. Lausanne, Switzerland: Presses Polytechniques Romandes.
Xiao, B., W. Wang, X. Zhang, G. Long, J. Fan, H. Chen, and L. Deng. 2019. “A novel fractal solution for permeability and Kozeny-Carman constant of fibrous porous media made up of solid particles and porous fibers.” Powder Technol. 349 (May): 92–98. https://doi.org/10.1016/j.powtec.2019.03.028.
Xu, P., and B. Yu. 2008. “Developing a new form of permeability and Kozeny–Carman constant for homogeneous porous media by means of fractal geometry.” Adv. Water Resour. 31 (1): 74–81. https://doi.org/10.1016/j.advwatres.2007.06.003.
Xu, Z., M. Lin, W. Jiang, G. Cao, and Z. Yi. 2020. “Identifying the comprehensive pore structure characteristics of a rock from 3D images.” J. Pet. Sci. Eng. 187 (Apr): 106764. https://doi.org/10.1016/j.petrol.2019.106764.
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Published In
Journal of Materials in Civil Engineering
Volume 35 • Issue 9 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 6, 2022
Accepted: Jan 30, 2023
Published online: Jun 19, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 19, 2023
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