Computational Optimization of Ceramic Waste-Based Concrete Mixtures: A Comprehensive Analysis of Machine Learning Techniques

This review examines the application of machine learning techniques for optimizing ceramic waste-based concrete, a sustainable alternative in construction. In the course of this work, numerous computational paradigms such as the Decision Trees, Random Forests, XGBoost, Artificial Neural Networks (ANNs), Bagging, AdaBoost, Gradient Boosting, Regression models as well as Support Vector Machines (SVMs) are discussed. Comparing to other models in this study, XGBoost and ANNs were shown to yield better results in terms of concrete properties hence revealing non-linear relationships in ceramic waste-concrete systems. However, there are also some shortcomings: small sample sizes were used, critical chemical features were not included, and critical hyperparameters were not tuned. The review emphasizes the need for larger, standardized datasets, incorporation of chemical composition data, and advanced techniques like deep learning and multi-objective optimization for future research. Such developments may further enhance the prediction precision and realism of the created model and subsequently ensure the long-lasting concrete through utilization of ceramic waste.