EXPERIMENTAL AND ANN-BASED INVESTIGATION OF RUBBERIZED GEOPOLYMER CONCRETE FOR SUSTAINABLE CONSTRUCTION
Abstract
This study develops a mix design for geopolymer concrete (GPC) incorporating waste tyre rubber as a partial replacement for fine aggregate. Standard cube and beam specimens were cast and tested for compressive strength, and the resulting experimental data were used to train an artificial neural network (ANN) model for strength prediction. The proposed AI-driven framework enables the early estimation of compressive strength, reducing reliance on extensive laboratory testing and supporting timely decision-making in material design and quality control. The ANN model achieved R2 values of 0.70, 0.42, and 0.57 on the training, validation, and test datasets, respectively, indicating moderate and consistent predictive performance. The network employs a two-layer feedforward architecture with seven input parameters, a sigmoid activation function in the hidden layer, and a linear output layer. While the model demonstrates reliable performance, further improvements through hyperparameter tuning and expanded datasets are anticipated. By integrating recycled tyre rubber into the GPC, the study addresses environmental and economic concerns, promotes sustainable construction practices, and supports circular-economy principles by valorising waste materials.
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