Title:
Integration of Automated Experiment and Data Science for Enhanced Evaluation of Cement Dispersant Performance
Author(s):
Jae Hong Kim and In Kuk Kang
Publication:
Symposium Paper
Volume:
369
Issue:
Appears on pages(s):
1-8
Keywords:
Automated Experimentation; Concrete; Rheology; Reproducibility; Data Science
DOI:
10.14359/51750716
Date:
5/1/2026
Abstract:
An innovative automated experimental system is proposed for precise evaluation of cement dispersant performance. The system enables comprehensive rheological measurements of 230 mL mortar samples without human intervention, overcoming limitations of traditional manual testing methods. Our automated experiment platform
incorporates a continuous-processing approach with precise control over sample preparation, including automated water and dispersant dosing via peristaltic pumps, programmed mixing sequences, and systematic rheological measurements through a specialized vane rotor system. The experimental sequence consists of dry mixing, water
incorporation, final high-speed mixing, and rheological measurements at controlled rotational speeds, all executed automatically with consistent timing and conditions. This approach reduces the labor-intensive nature of conventional testing while enabling the collection of substantially more data points for comprehensive analysis. The system allows for finer increments in dispersant dosage evaluation and eliminates variations associated with manual handling, thereby providing more reliable and reproducible results. Combined with data science techniques including principal component analysis and observation-informed learning, this automated framework establishes a new paradigm for cement-based materials characterization and quality assessment, suitable for both research and industrial applications
in concrete technology.
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