Enhancing concrete sustainability: a neural networks hybrid optimization approach to predicting compressive strength using supplementary cementitious materials

Abstract

This research evaluates the implementation of advanced machine learning methodologies for concrete mix design to achieve better predictive models and sustainable outcomes. This study develops a hybrid optimization approach by combining dung beetle optimizer (DBOA) and firefly algorithm (FLA) to optimize hyperparameters for convolutional-recurrent neural networks in order to correctly predict concrete compressive strength when using supplementary cementitious materials (SCMs). Shapley additive explanations (SHAP) provide feature significance analysis, which ensures that the model produces understandable conclusions supported by empirical findings. The findings demonstrate that this method enhances the predictive accuracy of strength analysis, along with offering critical insights about SCM usage in order to improve sustainable construction methods. The model proves suitable for integration into actual concrete mix design and quality control systems because it achieves both computational speed and passes validation tests on distinct datasets. The research creates foundations for upcoming studies about multimodal learning enrichment and deals with ethical concerns in construction site safety when using machine learning systems.