Authors : Aterezi Maro Clement; Odua Amaechi; Aule Solomon

Volume/Issue : Volume 11 - 2026, Issue 4 - April

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Google Scholar : https://tinyurl.com/38r8fv3b

Scribd : https://tinyurl.com/2s3w4m36

DOI : https://doi.org/10.38124/ijisrt/26apr172

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Abstract : This study develops and validates an innovative predictive model for estimating mechanical properties of polymer concrete using advanced machine learning techniques. Data from 180 polymer concrete samples across multiple sources were analyzed to predict compressive strength, tensile strength, and flexural strength. The research employed Gradient Boosting, Random Forest, Support Vector Machine, and Linear Regression algorithms. Results demonstrated that the Gradient Boosting model achieved superior performance with R² = 0.94, RMSE = 4.85 MPa, and MAE = 2.97 MPa for compressive strength prediction. Sensitivity analysis revealed polymer type (importance score: 0.35), aggregate size (0.25), and curing conditions (0.20) as the most influential parameters.

Keywords : Polymer Concrete, Machine Learning, Gradient Boosting, Mechanical Properties Prediction, Sustainable Construction.

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