Toward presenting an ensemble meta-model for evaluation of pozzolanic mixtures incorporating industrial by-products

Unsustainable development in the industry has increased the challenge of proper disposal of industrial by-products and has caused environmental pollution on a large scale. Furthermore, minimizing the carbon dioxide emissions due to industrial and environmental activities is a crucial challenge to controlling the global warming crisis and climate change. The use of industrial by-product-based supplementary cementitious materials (SCMs) can reduce fossil fuel consumption through the reduction in cement production and contribute to suitable waste management. In this research, a meta-model was developed using integration of the decision tree-based method named reduced error pruning tree (REPtree) and ensemble approaches (boosting and bagging) to improve the pruning phase for strength simulation of concrete mixtures incorporating industrial by-products. Then, ensemble multiple learning algorithms were applied to develop a meta-model using the artificial neural network and Gaussian process regression. A comprehensive experimental-based database incorporating 3836 records from short-term to long-term compressive strength (CS) was collected from the literature. Performance measures present that the proposed ensemble boosting-REPtree model outperforms other benchmark and ensemble models with a correlation coefficient of 0.960 and root mean square error of 7.884 MPa. Also, uncertainty evaluation was done based on the Monte Carlo simulation algorithm in the modeling process. The FAST and SOBOL methods, as global sensitivity analysis, presented water-to-binder (W/B) ratio, curing time, and SCMs to binder ratio (SCM/B) as critical components to estimate the CS of concrete mixtures. The computational simulation of the influential parameters on concrete mixtures incorporating industrial by-products revealed that the experimental factors yielding the highest CS of the mixtures were calculated as follows: cement content of 584 kg/m(3), W/B ratio of 0.17, superplasticizer-to-cement ratio of 1.88%, and SCM/B ratio of 0.15.