Python-Driven Optimization of Chikoko Admixture in High- Strength Concrete Production
Abstract
This study investigates the optimization of Chikoko admixture in high-strength concrete production through a Python-driven approach. The effects of various Chikoko concentrations (0%, 5%, 10%, 15%, and 20%) on compressive strength were evaluated across different curing temperatures (200°C, 400°C, 600°C, and 800°C) over extended curing times (7, 14, 28, 56, 90, and 120 days). Data collected from the experiments were analyzed using machine learning techniques to identify optimal Chikoko concentrations and curing conditions that maximize compressive strength and Strength Activity Index (SAI). Python-based machine learning models, including linear regression, decision tree regression, random forest regression, and gradient boosting, were employed to model the relationship between Chikoko concentration, curing temperature, and compressive strength. The findings suggest that curing temperature and Chikoko concentration significantly influence concrete performance, with optimal results achieved at 10% Chikoko and curing temperatures of 600°C–800°C.
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