AI-driven forecasting and laboratory evaluation of compressive strength in recycled aggregate concrete
DOI:
https://doi.org/10.21041/ra.v16i2.967Keywords:
recycled aggregate concrete, compressive strength, machine learning, support vector regression, sustainable construction, data-driven modelingAbstract
This study aims to evaluate and predict the compressive strength of recycled aggregate concrete (RAC) using experimental testing and machine learning techniques. Twenty-five concrete mixes with varying recycled aggregate content, water cement ratio, plasticizer dosage, and parent concrete strength were investigated. The 28-day compressive strength ranged from 31.8 to 45.2 MPa, decreasing with higher recycled aggregate content and water absorption. Support Vector Regression achieved the highest prediction accuracy (R2 = 0.998), outperforming other models. The study is limited by dataset size and controlled data expansion. The originality lies in integrating experimental investigation with multi-model machine learning analysis. The results demonstrate that AI can effectively support sustainable mix design of recycled aggregate concrete.
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