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Quantifying soil complexity using network models of soil porous structure

Marko Samec (1) , Antonio Santiago (2) , Juan Pablo Cardenas (2) , Rosa Maria Benito (2) , Ana Maria Tarquis (3) , Sacha Jon Mooney (4) , Dean Korošak (1,5). Quantifying soil complexity using network models of soil porous structure.

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Quantifying soil complexity using network models of soil porous structure

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  1. Marko Samec (1), Antonio Santiago (2), Juan Pablo Cardenas (2), Rosa Maria Benito (2), Ana Maria Tarquis (3), Sacha Jon Mooney (4), Dean Korošak (1,5) Quantifying soil complexity using network models of soil porous structure • (1) Faculty of Civil Engineering, University of Maribor, Maribor, Slovenia (marko.samec@gmail.com) • (2) Grupo de Sistemas Complejos, Departamento de Física, Universidad Politécnica de Madrid, 28040 Madrid, Spain • (3) Departamento de Matemática Aplicada, Universidad Politécnica de Madrid, 28040 Madrid, Spain • (4) Environmental Sciences Section,University of Nottingham, Nottingham, UK • (5) Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia

  2. porous matter

  3. porous matter • 3D X-ray CT

  4. porous matter • complex network theory

  5. porous matter • complexity hsingle parameter

  6. how can one see into the porous structureof material ? X-RAY COMPUTED TOMOGRAPHY

  7. measuring cell pore space solid space X-ray COMPUTED TOMOGRAPHY • part of x-rays absorbed • higher density of the matter, more X-rays are absorbed • the ones that manage to pass, create image

  8. X-ray COMPUTED TOMOGRAPHY concept

  9. X-ray COMPUTED TOMOGRAPHY image analysis • tomography image • crop and scale • contrast • threshold algorithm • binary image

  10. X-ray COMPUTED TOMOGRAPHY image analysis

  11. how can one describethe porous structureof material ? COMPLEX NETWORK THEORY

  12. BEGININGS • Seven Bridges of Königsberg, Euler (1735) • sociogram, Moreno (1993) • p53 gene network , Volgestein et al. (2000) • medicine, biology, computer science, particle physics, economics, sociology, ecology, epidemology, neuroscience,...

  13. BASICS • node or vertex • link or edge • direction • weight • node degree • degree distribution

  14. BASICS • Erdos-Renyi network (random linking) • scale-free networks (preferential linking) degree distribution

  15. X-ray COMPUTED TOMOGRAPHY image analysis Cumulative size distributions, determined from X-CT images of different porosity φ. All samples show scale-free behaviour of size distribution (solid line) with the exponent α=1,8-2.

  16. NETWORK MODELS for description of porous structure of building materials • threshold network model (SN) • preferential attachement model (EN) the network heterogeneity can be adjusted by changing the parameters b and m

  17. can one quantifydifferent porous structureof material ? COMPLEXITY

  18. COMPLEXITY correlation matrices to further explore the effect of the network parameter mon node correlations we calculated the complexityh of the pore network based on node-node link correlations

  19. COMPLEXITY to further explore the effect of the network parameter m on node correlations we calculated the complexityhof the pore network based on node-node link correlations

  20. by combining X-ray CT andcomplex network models • we can quantifycomplexityof porous structure with single parameter CONCLUSIONS

  21. Marko Samec (1), Antonio Santiago (2), Juan Pablo Cardenas (2), Rosa Maria Benito (2), Ana Maria Tarquis (3), Sacha Jon Mooney (4), Dean Korošak (1,5) Quantifying soil complexity using network models of soil porous structure (1) Faculty of Civil Engineering, University of Maribor, Maribor, Slovenia (marko.samec@gmail.com) (2) Grupo de Sistemas Complejos, Departamento de Física, Universidad Politécnica de Madrid, 28040 Madrid, Spain (3) Departamento de Matemática Aplicada, Universidad Politécnica de Madrid, 28040 Madrid, Spain (4) Environmental Sciences Section,University of Nottingham, Nottingham, UK (5) Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia

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