Automated estimation of cementitious sorptivity via computer vision

Hossein Kabir, Jordan Wu, Sunav Dahal, Tony Joo and Nishant Garg ()
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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

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-53993-w

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