A MACHINE LEARNING MODEL FOR PREDICTING THE COMPRESSIVE STRENGTH OF CEMENT MORTAR ADMIXED WITH MINERAL MATERIAL

Cornelius T. Iaren, Victor O. Uchola, Emmanuel  Osawe

Abstract


This study investigated the use of pulverized waste glass as a partial replacement for cement in mortar and develops an artificial neural network (ANN) model to predict compressive strength at 28 days. Experimental data indicated that compressive strength increased from 24.50 MPa (control) to 29.08 MPa at 15% glass replacement. To overcome the limitation of small datasets, 200 augmented samples per replacement level were generated, resulting in an 800-sample dataset from existing lab results. A comparison was made between the augmented data and the lab results, it’s correlation was equally good at good fit of 85%. The ANN, trained using cement, water, sand, and glass content as inputs, achieved strong predictive accuracy (R² = 0.85, RMSE = 0.71 MPa, MAE = 0.58 MPa). Results confirm that pulverized waste glass can enhance mortar strength while reducing cement demand, offering both economic and environmental benefits. The ANN model provides a reliable computational approach for strength prediction and can support design optimization in sustainable construction.

 

KEYWORDS: Cement, Computation, Modelling, Artificial Neural Network, Machine Learning, Mineral Material


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References


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