Application of a comparative analysis of random forest programming to predict the strength of environmentally-friendly geopolymer concrete

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (𝐺𝑃𝐶) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce 𝐶𝑂2 emissions in the building industry. In the present work, the compressive strength (𝑓𝑐 ) of 𝐺𝑃𝐶 is calculated using random forests regression (𝑅𝐹𝑅) methodology where natural zeolite (𝑁𝑍) and silica fume (𝑆𝐹) replace ground granulated blast-furnace slag (𝐺𝐺𝐵𝐹𝑆). From the literature, a thorough set of experimental experiments on 𝐺𝑃𝐶 samples were compiled, totaling 254 data rows. The considered 𝑅𝐹𝑅 integrated with artificial hummingbird optimization (𝐴𝐻𝐴), black widow optimization algorithm (𝐵𝑊𝑂𝐴), and chimp optimization algorithm (𝐶ℎ𝑂𝐴), abbreviated as 𝐴𝑅𝐹𝑅, 𝐵𝑅𝐹𝑅, and 𝐶𝑅𝐹𝑅. The outcomes obtained for 𝑅𝐹𝑅 models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For 𝑅 2 metric, the 𝐶𝑅𝐹𝑅 model gained 0.9988 and 0.9981 in the train and test data set higher than those for 𝐵𝑅𝐹𝑅 (0.9982 and 0.9969), followed by 𝐴𝑅𝐹𝑅 (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for 𝐶𝑅𝐹𝑅 respect to 𝐴𝑅𝐹𝑅.