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

Abstract

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 R2 score and root mean square error (RMSE).

Artificial Neural Networks for Sustainable Development of the Construction Industry

“…An ANN model using sieve analysis, Atterberg limits, optimum moisture content, and maximum dry density data was applied by Bhatt and Jain [90] to predict the California bearing ratio of soils. Mahamat et al [91] predicted the mechanical characteristics of the soil in termite mound soil activated by alkali. Nazemi et al [92] predicted the absorption and volume of water in porous construction materials by applying the ANN approach.…”

Dimensionnal stability and strength appraisal of termite hill soil stabilisation using hybrid bio-waste and cement for eco-friendly housing

“…Earth-based construction has been used empirically. However, it's necessary to develop technologies [ 7 ] to transform earth-based materials into strong and durable construction materials [ 8 ] and search for alternatives materials to serve as binder [ 9 , 10 ]. The alternatives are preferably industrial [ 11 ] or agro-waste [ 12 ] materials, as their disposal constitutes a considerable factor in the ecosystem's stability [ 13 ].…”