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

Applied Sciences

Holmer Savastano¹  MoussaMahamat Boukar²  NurudeenMahmud Ibrahim²  AssiaAboubakar Mahamat³  IfeyinwaIjeoma Obianyo³  TidoTiwa Stanislas³  Numfor Linda Bih³ 

[1] Department of Biosystems Engineering, Research Nucleus on Materials for Biosystems NAP BioSMat, University of Sao Paulo, Sao Paulo 13635-900, Brazil;Department of Computer Science, Faculty of Natural and Applied Sciences, Nile University of Nigeria, Abuja 900100, Nigeria;Department of Materials Science and Engineering, African University of Science and Technology, Abuja 900100, Nigeria;

关键词: machine learning;    artificial neural network;    support vector machine;    linear regression;    alkali-activated termite soil;    compressive strength;   

DOI  :  10.3390/app11114754

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown