SorpVision: A Comprehensive Dataset for Cementitious Sorptivity Analysis Powered by Computer Vision
Year: 2025
Type: data-paper
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 2 > 0.9 for cement pastes). Moreover, it generalizes well to mortar and concrete, yielding R 2 values of 0.96 and 0.87 for initial sorptivity and 0.74 and 0.65 for secondary sorptivity, respectively. SorpVision offers an accurate, data-driven foundation for scalable, automated durability evaluations, supporting sustainable infrastructure development.
Source: Scientific Data
Authors: Hossein Kabir, Jordan Wu, Sunav Dahal, Tony Joo, Nishant Garg
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W4410866296