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Effect of Global Thresholding Algorithms on Pervious Concrete Pore Network Properties Using XRCT-Based Digital Image Processing

  • Ajayshankar Jagadeesh
  • Ghim Ping OngEmail author
  • Yu-Min Su
Conference paper
  • 110 Downloads
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 76)

Abstract

Digital image processing of the X-ray computed tomography images involves the crucial step of image segmentation which affects the subsequent pore structure quantitative analysis. The main objective of this study is to investigate the effect of ten different global thresholding algorithms based on the grey scale histogram, clustering, entropy and laboratory volumetric characteristics on the internal pore structure properties of the pervious concrete. The key microstructural parameters of the pervious concrete air voids such as porosity, tortuosity, throat number, pore coordination number and distributions of pore volume, throat area, pore sphericity, shape factor and throat eccentricity were analyzed for different thresholding algorithms. It was found from the analysis that the nine histogram, clustering and entropy based algorithms are found to be either under or over estimating the air void voxels compared to the volumetric segmentation method. And as the threshold value increases, effective porosity and number of throats increases and isolated porosity and tortuosity decreases due to the increase of air void voxels and pore connectivity. Overall, it is expected that the present study will help in understanding the importance of threshold segmentation in the field of pavement image processing.

Keywords

Pervious concrete pores X-ray computed tomography Global thresholding Volumetric segmentation 

References

  1. Abera KA, Manahiloh KN, Nejad MM (2017) The effectiveness of global thresholding techniques in segmenting two-phase porous media. Constr Build Mater 142:256–267CrossRefGoogle Scholar
  2. ACI 522R (2010) Report on Pervious Concrete. American Concrete InstituteGoogle Scholar
  3. ASTM (2012) Standard test method for density and void content of hardened pervious concrete. ASTM C1754-12/C1754M. West Conshohocken. ASTM, PAGoogle Scholar
  4. Chandrappa AK, Biligiri KP (2018) Pore structure characterization of pervious concrete using X-ray microcomputed tomography. J Mater Civ Eng 30(6):04018108CrossRefGoogle Scholar
  5. Iassonov P, Gebrenegus T, Tuller M (2009) Segmentation of X-ray computed tomography images of porous materials: a crucial step for characterization and quantitative analysis of pore structures. Water Resour Res 45(9)Google Scholar
  6. Jagadeesh A, Ong GP, Su YM (2018a) Porosity and permeability evaluation of pervious concrete using three dimensional X-ray computed tomography. In: 4th international conference on transport infrastructure, pretoria, South Africa, JulyGoogle Scholar
  7. Jagadeesh A, Ong GP, Su YM (2018b) Digital sieving of pervious concrete air voids using X-ray computed tomography. In: 11th Asia pacific transportation and the environment conference, Malang, Indonesia, OctoberGoogle Scholar
  8. Jagadeesh A, Ong GP, Su YM (2019a) Development of discharge-based thresholding algorithm for pervious concrete pavement mixtures. J Mater Civ Eng 31(9):04019179CrossRefGoogle Scholar
  9. Jagadeesh A, Ong GP, Su YM (2019b) Evaluation of pervious concrete pore network properties using watershed segmentation approach. In: Airfield and highway pavements 2019: testing and characterization of pavement materials. American Society of Civil Engineers, Reston, pp 437–446Google Scholar
  10. Jagadeesh A, Ong GP, Su YM (2019c) Texture evaluation of pervious pavements using three-dimensional X-ray computed tomography. In: 11th international conference on road and airfield pavement technology, Kuala Lumpur, Malaysia, JulyGoogle Scholar
  11. Kuang X, Ying G, Ranieri V, Sansalone J (2015) Examination of pervious pavement pore parameters with X-ray tomography. J Environ Eng 141(10):04015021CrossRefGoogle Scholar
  12. Mahmud MZH, Hassan NA, Hainin MR, Ismail CR (2017) Microstructural investigation on air void properties of porous asphalt using virtual cut section. Constr Build Mater 155:485–494CrossRefGoogle Scholar
  13. Manahiloh KN, Muhunthan B, Kayhanian M, Gebremariam SY (2012) X-ray computed tomography and nondestructive evaluation of clogging in porous concrete field samples. J Mater Civ Eng 24(8):1103–1109CrossRefGoogle Scholar
  14. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–166CrossRefGoogle Scholar
  15. Zelelew HM, Papagiannakis AT (2011) A volumetrics thresholding algorithm for processing asphalt concrete X-ray CT images. Int J Pavement Eng 12(6):543–551CrossRefGoogle Scholar
  16. Zhang J, Ma G, Ming R, Cui X, Li L, Xu H (2018) Numerical study on seepage flow in pervious concrete based on 3D CT imaging. Constr Build Mater 161:468–478CrossRefGoogle Scholar
  17. Zhang Y, Verwaal W, Van de Ven MFC, Molenaar AAA, Wu SP (2015) Using high-resolution industrial CT scan to detect the distribution of rejuvenation products in porous asphalt concrete. Constr Build Mater 100:1–10CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ajayshankar Jagadeesh
    • 1
  • Ghim Ping Ong
    • 1
    Email author
  • Yu-Min Su
    • 2
  1. 1.Department of Civil and Environmental EngineeringNational University of SingaporeSingaporeSingapore
  2. 2.Department of Civil EngineeringNational Kaohsiung University of Science and TechnologyKaohsiungTaiwan

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