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Automated estimation of cementitious sorptivity via computer vision

Author

Listed:
  • Hossein Kabir

    (University of Illinois at Urbana-Champaign)

  • Jordan Wu

    (University of Illinois at Urbana-Champaign)

  • Sunav Dahal

    (University of Illinois at Urbana-Champaign)

  • Tony Joo

    (University of Illinois at Urbana-Champaign)

  • Nishant Garg

    (University of Illinois at Urbana-Champaign)

Abstract

Monitoring water uptake in cementitious systems is crucial to assess their durability against corrosion, salt attack, and freeze-thaw damage. However, gauging absorption currently relies on labor-intensive and infrequent weight measurements, as outlined in ASTM C1585. To address this issue, we introduce a custom computer vision model trained on 6234 images, consisting of 4000 real and 2234 synthetic, that automatically detects the water level in prismatic samples absorbing water. This model provides accurate and frequent estimations of water penetration values every minute. After training the model on 1440 unique data points, including 15 paste mixtures with varying water-to-cement ratios from 0.4 to 0.8 and curing periods of 1 to 7 days, we can now predict initial and secondary sorptivities in real time with high confidence, achieving R² > 0.9. Finally, we demonstrate its application on mortar and concrete systems, opening a pathway toward low-cost and automated durability assessment of construction materials.

Suggested Citation

  • Hossein Kabir & Jordan Wu & Sunav Dahal & Tony Joo & Nishant Garg, 2024. "Automated estimation of cementitious sorptivity via computer vision," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53993-w
    DOI: 10.1038/s41467-024-53993-w
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    References listed on IDEAS

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    1. Alexander Radovic & Mike Williams & David Rousseau & Michael Kagan & Daniele Bonacorsi & Alexander Himmel & Adam Aurisano & Kazuhiro Terao & Taritree Wongjirad, 2018. "Machine learning at the energy and intensity frontiers of particle physics," Nature, Nature, vol. 560(7716), pages 41-48, August.
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