ArticleNature communications2024
Automated estimation of cementitious sorptivity via computer vision.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Optimizing the Tensile Performance of Repaired CFRP Laminates with Different Patch Parameters Using a Surrogate-Based Model.Materials (Basel, Switzerland) · 2025Article
- Degradation Characteristics and Service Life Prediction of Desert Sand Concrete Under Load and Freeze-Thaw Conditions.Materials (Basel, Switzerland) · 2025Article
- Water-Soaking Pretreatment for Enhanced Performance and Heavy Metal Immobilization in Alkali-Activated Pyrolysis MSWIFA Materials.Materials (Basel, Switzerland) · 2025Article
- Bayesian LSTM-Based Missing Data Estimation and Flexural Strength Assessment for Determination of Novel Smart Mobility Pavement Materials: Mg(OH)Materials (Basel, Switzerland) · 2025Article
- Influence of Mullite and Halloysite Reinforcement on the Ablation Properties of an Epoxy Composite.Materials (Basel, Switzerland) · 2025Article
- Causal association between cathepsins and asthma: a Mendelian randomization study.Scientific reports · 2025Article
- An Interpretability Method for Broken Wire Detection.Sensors (Basel, Switzerland) · 2025Article
- SorpVision: A Comprehensive Dataset for Cementitious Sorptivity Analysis Powered by Computer Vision.Scientific data · 2025Article
- The Optimization Study of Karst-Filling Clay-Cement Grout Based on Orthogonal Experiment and Regression Analysis.Materials (Basel, Switzerland) · 2025Article
- Associations and mediators between vitiligo and cardiovascular diseases: a Mendelian randomization study.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.