Prediction of sustainable concrete utilizing rice husk ash (RHA) as supplementary cementitious material (SCM): Optimization and hyper-tuning

Rice Husk ash (RHA) utilization in concrete as a waste material can contribute to the formation of a robust cementitious matrix with utmost properties. The strength of HPC when subjected to compression test is determined by the combination and quantity of the materials used in its production. Thus, making its mixed design process challenging and ambiguous. The objective of this research is to forecast the strength of HPC containing RHA, by using a diverse range of machine learning techniques, including both individual and ensemble learners such as bagging and boosting. This study will cause significant implications for sustainable construction practices by facilitating the development of an efficient and effective method for forecasting the strength of HPC. Individual machine learning (ML) algorithms are incorporated with ensemble methods such as bagging, adaptive boosting, and random forest algorithms. These ensemble techniques is use to create twenty different sub-models. Afterward, these sub-models is train and optimized for achieving the best possible value for R2. The sub-models were further fine-tuned to obtain the best value for R2. In order to assess or evaluate the quality, reliability, and generalizability of the test data, the K-Fold cross-validation method is utilized. Furthermore, the index for measuring the statistical performance of models is use to validate and compare the assessment of ensemble models with individual models. The findings indicate that using bagging and boosting techniques enhances the prediction accuracy of individual or weak models, with minimum errors and an R2 value > 0.92 is achieved using bagging with decision tree and random forest. In general, the performance of the model is optimized by using ensemble learner methods in machine learning (ML).