Machine Learning Approaches for Prediction of the Compressive Strength of Alkali Activated Termite Mound Soil

Featured Application The potential application of the work is to facilitate the perception of the properties of unconventional construction materials. That implies the correlation between the various constituent during the prediction. Earth-based materials have shown promise in the development of ecofriendly and sustainable construction materials. However, their unconventional usage in the construction field makes the estimation of their properties difficult and inaccurate. Often, the determination of their properties is conducted based on a conventional materials procedure. Hence, there is inaccuracy in understanding the properties of the unconventional materials. To obtain more accurate properties, a support vector machine (SVM), artificial neural network (ANN) and linear regression (LR) were used to predict the compressive strength of the alkali-activated termite soil. In this study, factors such as activator concentration, Si/Al, initial curing temperature, water absorption, weight and curing regime were used as input parameters due to their significant effect in the compressive strength. The experimental results depict that SVM outperforms ANN and LR in terms of R-2 score and root mean square error (RMSE).

Automated summary

The unconventional usage of earth-based materials in construction makes it challenging to accurately estimate their properties, with traditional materials procedures falling short in accuracy. To predict the compressive strength of these materials, support vector machines, artificial neural networks, and linear regression models were used, with support vector machines outperforming the other methods in terms of R-2 score and root mean square error.