Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-18T02:00:50.207Z Has data issue: false hasContentIssue false
  • English
  • Français

Materials Data Science for Microstructural Characterization of Archaeological Concrete

Published online by Cambridge University Press:  24 February 2020

Daniela Ushizima ,
Ke Xu  and
Paulo J.M. Monteiro
Daniela Ushizima*
Affiliation:
University of California Berkeley, Berkeley, CA 94720 Lawrence Berkeley National Laboratory, Berkeley, CA 94720
Ke Xu
Affiliation:
University of California Berkeley, Berkeley, CA 94720 Lawrence Berkeley National Laboratory, Berkeley, CA 94720
Paulo J.M. Monteiro
Affiliation:
University of California Berkeley, Berkeley, CA 94720 Lawrence Berkeley National Laboratory, Berkeley, CA 94720
*
*(Email: [email protected])
Get access

Abstract

Ancient Roman concrete presents exceptional durability, low-carbon footprint, and interlocking minerals that add cohesion to the final composition. Understanding of the structural characteristics of these materials using X-ray tomography (XRT) is of paramount importance in the process of designing future materials with similar complex heterogeneous structures. We introduce Materials Data Science algorithms centered on image analysis of XRT that support inspection and quantification of microstructure from ancient Roman concrete samples. By using XRT imaging, we access properties of two concrete samples in terms of three different material phases as well as estimation of materials fraction, visualization of the porous network and density gradients. These samples present remarkable durability in comparison with the concrete using Portland cement and nonreactive aggregates. Internal structures and respective organization might be the key to construction durability as these samples come from ocean-submersed archeological findings dated from about two thousand years ago. These are preliminary results that highlight the advantages of using non-destructive 3D XRT combined with computer vision and machine learning methods for systematic characterization of complex and irreproducible materials such as archeological samples. One significant impact of this work is the ability to reduce the amount of data for several computations to be held at minimalistic computational infrastructure, near real-time, and potentially during beamtime while materials scientists are still at the imaging facilities.

Keywords

x-ray tomographymetrologymicrostructurearcheology
Type
Articles
Information
MRS Advances , Volume 5 , Issue 7: BROADER IMPACT , 2020 , pp. 305 - 318
Copyright
Copyright © Materials Research Society 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES:

Andrew, R., Global CO2 emissions from cement production, Earth System Science Data, 10, 195–217, (2018).CrossRefGoogle Scholar
Williams, F., Concrete responsible for 8 percent of all CO2 emissions, https://www.architectsjournal.co.uk/news/concrete-responsible-for-8-per-cent-of-all-co2-emissions-says-report/10038404.article (accessed 10 December 2019).Google Scholar
Mittelman, E., The Cement Industry, One of the World’s Largest CO2 Emitters, Pledges to Cut Greenhouse Gases, https://e360.yale.edu/digest/the-cement-industry-one-of-the-worlds-largest-co2-emitters-pledges-to-cut-greenhouse-gases (accessed 10 December 2019).Google Scholar
Jackson, M., Sejung, C., Mulcahy, S., Meral Akgul, C., Taylor, R., Li, P., Emwas, A., Moon, J., Yoon, S., Vola, G., Wenk, H., Monteiro, P., Unlocking the secrets of Al-tobermorite in Roman seawater concrete. American Mineralogist. 98. 1669-1687. 10.2138/am.2013.4484, (2013).CrossRefGoogle Scholar
Jackson, M. D., Mulcahy, S. R., Chen, H., Li, Y., Li, Q., Cappelletti, P., Rudolf Wenk, H., American Mineralogist 102 (7): 1435-1450, (2017).CrossRefGoogle Scholar
Goodfellow, I., Bengio, Y., Courville, A., Deep Learning, MIT Press (2016).Google Scholar
Ushizima, D. M., Bale, H. A., Bethel, E. W., Ercius, P., Helms, B., Krishnan, H., Grinberg, L. T., Haranczyk, M., Macdowell, A. A., Odziomek, K., Parkinson, D. Y., Terciano, T., Ritchie, R. O., Yang, C.. IDEAL: Images across domains, experiments, algorithms and learning. The Journal of The Minerals, Metals & Materials Society, pages 1–10, Sep (2016).Google Scholar
Liu, S., Melton, C. N., Venkatakrishnan, S., Pandolfi, R., Freychet, G., Kumar, D., Tang, H., Hexemer, A., Ushizima, D. M., Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification, (9)2: 586-592, (2019).Google Scholar
Bale, H. A., Haboub, A., MacDowell, A. A., Nasiatka, J. R., Parkinson, D. Y., Cox, B. N., Marshall, D. B. and Ritchie, R. O., Nature Materials 12: 40–46, (2013).CrossRefGoogle Scholar
Willemink, M. and Noel, P., The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence, European Radiology, 29:10, (2018).Google ScholarPubMed
Monteiro, P. J. M., Pichot, C. Y., and Belkebir, K.. Computer tomography of reinforced concrete. In Materials Science of Concrete, American Ceramics Society, volume 5: pages 537–572, (1998).Google Scholar
Brandon, C., Hohlfelder, R. L., Jackson, M. D., and Oleson, J. P., Building for Eternity: The History and Technology of Roman Concrete Engineering in the Sea. Oxbow Books, Oxford, page 327, (2014).CrossRefGoogle Scholar
Lawrence Berkeley National Laboratory Advanced Light Source Beamline 8.3.2.http://microct.lbl.gov/ (accessed 10 December 2019).Google Scholar
Gürsoy, D., De Carlo, F., Xiao, X., and Jacobsen, C., Tomopy: a framework for the analysis of synchrotron tomographic data, Journal of Synchrotron Radiation, 21(5):1188–1193, (2014).CrossRefGoogle ScholarPubMed
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E. and Yu, T., scikit-image: image processing in Python.” PeerJ vol. 2: e453, (2014).CrossRefGoogle ScholarPubMed
Gouillart, E., Segmentation of 3-D tomography images with Python and scikit-image, September (2015). http://emmanuelle.github.io/segmentation-of-3-d-tomography-images-with-python-and-scikit-image.html (accessed 10 December 2019).Google Scholar
Miramontes-Lizarraga, S., Ushizima, D., Parkinson, D., Evaluating fiber detection models using Neural Networks, ISVC’19, (2019).CrossRefGoogle Scholar
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P. and Süsstrunk, S., SLIC superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence, 34(11): 2274-2282, (2012).CrossRefGoogle ScholarPubMed
Sato, Y., Nakajima, S., Atsumi, H., Roller, T., Gerig, G., Yoshida, S., Kikinis, R., 3D multi-scale line filler for segmentation and visualization of curvilinear structures in medical images. In Troccaz, J., Grimson, E., and Mösges, R., eds., Proc. CVRMed-MRCAS’97, LNCS, pages213–222, (1997).CrossRefGoogle Scholar
Ojala, T., Pietikäinen, M., and Harwood, D., Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582 - 585, (1994).CrossRefGoogle Scholar
Breiman, L., Random Forests, Machine Learning, 45(1): 5–32, (2001).CrossRefGoogle Scholar
Witten, I., Frank, E., and Hall, M., Data Mining: Practical Machine Learning Tools and Techniques, 3 edition, (2011).Google Scholar
Kumar, N., Khatri, S., Implementing WEKA for medical data classification and early disease prediction, IEEE International Conference on Computational Intelligence & Communication Technology, (2017).CrossRefGoogle Scholar
Xu, K., Monteiro, P., Ushizima, D., Unveiling the secrets of Roman Concrete with Computer Vision, 2018 SSRL/LCLS Users’ Meeting, (2018).Google Scholar
Ushizima, D. and de Siqueira, A., “scikit-image: 3D Image Processing”, Image Analysis across Domains, ImageXD’2019, Berkeley, CA. https://github.com/imagexd/2019-tutorial-skimage. (accessed 10 December 2019).Google Scholar
Brisard, S., Serdar, M., Monteiro, P. J. M., “Multiscale X-ray tomography of cementitious materials: A review”, Cement and Concrete Research 128 (2020).CrossRefGoogle Scholar