SorpVision: A Comprehensive Dataset for Cementitious Sorptivity Analysis Powered by Computer Vision.

Hossein Kabir, Jordan Wu, Sunav Dahal, Tony Joo, Nishant Garg

Abstract read

In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Hossein KabirDepartment of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.ORCID http://orcid.org/0000-0002-2222-6188

Jordan WuDepartment of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

Sunav DahalDepartment of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.ORCID http://orcid.org/0009-0009-5727-2713

Tony JooDepartment of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

Nishant GargDepartment of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. nishantg@illinois.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the construction industry advances toward more efficient methods for assessing durability, the need for automated sorptivity evaluation has become increasingly critical. Consequently, this study introduces SorpVision, a dataset of 7,384 images (5,000 real and 2,384 synthetic) designed to support our custom computer vision-based framework for automated sorptivity evaluation in cementitious materials. Traditional methods, such as ASTM C1585, depend on manual weighing, which is time-consuming and limits measurement intervals. SorpVision, combined with a cost-effective USB camera setup and a robust vision algorithm, facilitates real-time water level detection in cementitious systems. The framework, trained using 1,440 data points from pastes with water-to-cement (w/c) ratios of 0.4-0.8 and curing durations of 1-7 days, achieves high predictive accuracy for initial and secondary sorptivities (R

Identifiers

PMID40442085

DOI10.1038/s41597-025-05185-4

PMCPMC12123020

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LicenceCC BY-NC-ND

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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.