Advertisement

A method for microstructure similarity clustering and feature reconstruction for weathered weak muddy intercalations

  • Qijun Hu
  • Tianjun He
  • Tao Ye
  • Qijie Cai
  • Songsheng He
  • Leping He
Original Paper
  • 52 Downloads

Abstract

Weak muddy intercalations (WMI) are a type of geo-material with highly unstable mechanical properties, and thus they pose a great threat to the stability of rock slopes and in other rock engineering situations. In this paper, microstructure similarity-based clustering together with image fusion and reconstruction are used to study the microstructures of shallow weathered WMI. The study aims to obtain a reconstructed image of microstructure features that can represent a region to provide the basis for subsequent studies on WMI mechanical properties. The similarity of each microscopic WMI image is calculated using a similarity calculation model based on the microstructure parameters, and images are clustered based on their similarities. Then, image fusion technology is used to combine images in the same cluster. The results are as follows: (1) Similarity corresponding to a cumulative distribution probability of 80% is used as the clustering threshold; (2) The fused WMI microstructure image can represent the microstructure of a layer in the sample. In view of these findings, WMI microstructure clustering and feature reconstruction can provide evidence for studies on WMI lamina structures and failures involving these, which formed the basis for the assessment of the stability of slopes and other situations in which WMI are present.

Keywords

Muddy weak intercalations Microstructure Similarity calculation model Clustering Image fusion 

Notes

Acknowledgments

The work was supported by the National Natural Science Foundation of China (Nos. 51574201) and the State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection (Chengdu University of Technology) (SKLGP2015K006). Additional support was provided by the Scientific and Technical Youth Innovation Group (Southwest Petroleum University) (2015CXTD05).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

References

  1. Biswas A, Rai A, Ahmad T, Sahoo PM (2017) Spatial estimation and rescaled spatial bootstrap approach for finite population. Commun Stat Theory Methods 46(1):373–388CrossRefGoogle Scholar
  2. Chen J, Dai F, Xu L, Chen S, Wang P, Long W, Shen N (2014) Properties and micro-structure of a natural slip zone in loose deposits of red beds, southwestern China. Eng Geol 183:53–64CrossRefGoogle Scholar
  3. Cotecchia F, Cafaro F, Guglielmi S (2016) Microstructural changes in clays generated by compression explored by means of SEM and image processing. Procedia Engineer 158:57–62Google Scholar
  4. Deza MM, Deza E (2009) Encyclopedia of distances. Springer, Berlin, p 1–583Google Scholar
  5. Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput Sci 54:764–771CrossRefGoogle Scholar
  6. Dudoignon P, Gélard D, Sammartino S (2004) Cam-clay and hydraulic conductivity diagram relations in consolidated and sheared clay-matrices. Clay Miner 39(3):267–279CrossRefGoogle Scholar
  7. Gerke KM, Karsanina MV, Mallants D (2015) Universal stochastic multiscale image fusion: an example application for shale rock. Sci Rep 5:15880CrossRefGoogle Scholar
  8. Gutierrez NHM, de Nobrega MT, Vilar OM (2009) Influence of the micro-structure in the collapse of a residual clayey tropical soil. Bull Eng Geol Environ 68(1):107–116CrossRefGoogle Scholar
  9. Gylland AS, Rueslåtten H, Jostad HP, Nordal S (2013) Microstructural observations of shear zones in sensitive clay. Eng Geol 163:75–88CrossRefGoogle Scholar
  10. Houben M, Desbois G, Urai J (2014) A comparative study of representative 2D micro-structures in shaly and sandy facies of Opalinus clay (Mont Terri, Switzerland) inferred form BIB-SEM and MIP methods. Mar Pet Geol 49:143–161CrossRefGoogle Scholar
  11. Jiang M, Zhang F, Hu H, Cui Y, Peng J (2014) Structural characterization of natural loess and remolded loess under triaxial tests. Eng Geol 181:249–260CrossRefGoogle Scholar
  12. Jiang C, Zhou X, Tao G, Chen D (2016) Experimental study on the performance and micro-structure of cementitious materials made with dune sand. Adv Mater Sci Eng.  https://doi.org/10.1155/2016/2158706
  13. Kawamura K, Ogawa Y, Oyagi N, Kitahara T, Anma R (2007) Structural and fabric analyses of basal slip zone of the Jin’nosuke-dani landslide, northern Central Japan: its application to the slip mechanism of décollement. Landslides 4(4):371–380CrossRefGoogle Scholar
  14. Keller LM, Schuetz P, Erni R, Rossell MD, Lucas F, Gasser P, Holzer L (2013) Characterization of multi-scale microstructural features in Opalinus clay. Microporous Mesoporous Mater 170:83–94CrossRefGoogle Scholar
  15. Kim SK, Kang ST, Kim JK, Jang IY (2017) Effects of particle size and cement replacement of LCD glass powder in concrete. Adv Mater Sci Eng.  https://doi.org/10.1155/2017/3928047
  16. Li H, Liu X, Yu Z, Zhang Y (2016) Performance improvement scheme of multifocus image fusion derived by difference images. Signal Process 128:474–493CrossRefGoogle Scholar
  17. Lin S-z, Wang D-j, Wang X-x, Zhu X-h (2016) Multi-band texture image fusion based on the embedded multi-scale decomposition andpossibility theory. Spectrosc Spectr Anal 36(7):2337–2343Google Scholar
  18. Liu C, Shi B, Zhou J, Tang C (2011) Quantification and characterization of microporosity by image processing, geometric measurement and statistical methods: application on SEM images of clay materials. Appl Clay Sci 54(1):97–106CrossRefGoogle Scholar
  19. Mishnaevsky LL, Schmauder S (2001) Continuum mesomechanical finite element modeling in materials development: a state-of-the-art review. Appl Mech Rev 54(1):49–67CrossRefGoogle Scholar
  20. Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. In: Jordan MI, LeCun Y, Solla SA (eds) Advances in neural information processing systems. MIT Press, Cambridge, pp 849–856Google Scholar
  21. Pandey S, Khanna P (2016) Content-based image retrieval embedded with agglomerative clustering built on information loss. Comput Electr Eng 54:506–521CrossRefGoogle Scholar
  22. Pusch R, Weston R (2003) Microstructural stability controls the hydraulic conductivity of smectitic buffer clay. Appl Clay Sci 23(1):35–41CrossRefGoogle Scholar
  23. Sivakumar V, Doran I, Graham J (2002) Particle orientation and its influence on the mechanical behaviour of isotropically consolidated reconstituted clay. Eng Geol 66(3):197–209CrossRefGoogle Scholar
  24. Tang Y-q, Zhou J, Hong J, Yang P, Wang J-x (2012) Quantitative analysis of the micro-structure of shanghai muddy clay before and after freezing. Bull Eng Geol Environ 71(2):309–316CrossRefGoogle Scholar
  25. Van Mier JGM, Van Vliet MRA (2003) Influence of microstructure of concrete on size/scale effects in tensile fracture. Eng Fract Mech 70(16):2281–2306CrossRefGoogle Scholar
  26. Wang C-l, Wang H-W, Hu B-l, Wen J, Xu J, Li X-J (2016) A novel spatial-spectral sparse representation for hyperspectral image classification based on neighborhood segmentation. Spectrosc Spectr Anal 36(9):2919–2924Google Scholar
  27. Xu B, Yan C, Xu S (2013) Analysis of the bedding landslide due to the presence of the weak intercalated layer in the limestone. Environ Earth Sci 70:2817–2825CrossRefGoogle Scholar
  28. Zheng L-N (2012) Reaserch of failure mechanism and the local failure zones for consequent slope based on strain softening theory. Southwest Jiao Tong University, ChengduGoogle Scholar
  29. Zheng Y, Liu J, Hu Q, Cai Q (2014) Study on micro-structure of muddy intercalation using SEM method. Electron J Geotech Eng 19:9953–9963Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Qijun Hu
    • 1
  • Tianjun He
    • 1
  • Tao Ye
    • 2
  • Qijie Cai
    • 3
  • Songsheng He
    • 1
  • Leping He
    • 1
  1. 1.School of Civil Engineering and ArchitectureSouthwest Petroleum UniversityChengduChina
  2. 2.Ranken Railway Construction Group Co., LtdChengduChina
  3. 3.School of Transportation and LogisticsSouthwest Jiaotong UniversityChengduChina

Personalised recommendations