## 5 Conclusions  
In this paper, the applicability of SVM, ANN and LR models for the prediction of alkali-activated termite soil's compressive strength were explored. It was demonstrated that the developed models were successfully trained and validated based on the experimental dataset. The three models developed were compared based on their accuracy. The following focal conclusions can be drawn:  
* Termite mound soil is an unconventional earth-based material, classified as natural pozzolanas. Its activation through naturally occurring alum is aimed to produce eco-friendly and locally available construction materials. Subsequently, the novelty of these materials makes the application of ML techniques a useful tool to appraise their properties with a variation of constituents.
* The correlation between the input parameters and the output feature displayed by the coefficient of determination R\({}^{2}\) (70%, 63% and 26%) indicates that the three models are suitable for modeling the compressive strength of the AATS dataset.
* The SVM model displayed the higher coefficient of determination (70%) and a root mean square of 0.6. These values indicate the accuracy of the model in predicting the compressive strength of the AATS based on the given input parameters.
* The ANN exhibited the second-best performance, with a coefficient of determination of 63% and a root mean square of 0.7.
* LR demonstrated the lower accuracy, with a coefficient of determination of 26% and a root mean square of 0.95. A lower mean square error is desirable; a high RMSEsignifies higher error. Therefore, the SVM and ANN perform better since they have a low RMSE compared to LR.  
The higher accuracy and suitability of the SVM model made it more desirable than the ANN and LR methods in the prediction of the AATS compressive strength. In addition to the high accuracy, the usage of the SVM model contributes to reduce time-consuming laboratory experiments, resulting in the reduction in the general cost and time during the properties' investigations. The regression analysis showed that all studied parameters in this work have considerable effects on the properties of the AATS. However, more analysis can be performed in future investigations to determine the most influential parameter.  
For future work, the applicability of the ML methods can be used to assess the properties of new unconventional materials with questionable features such as new mixture constituents. In addition, the experiment was conducted with small scale data. In future research, it is recommended to expand the data scale in order to examine the performance of the algorithms with large scale data in the domain.  
Conceptualization, A.A.M., M.M.B. and N.L.B.; methodology, A.A.M., M.M.B. and H.S.J.; software, N.M.I.; supervision, M.M.B. and H.S.J.; visualization, T.T.S., I.I.O. and H.S.J.; writing--original draft, A.A.M., N.M.I., T.T.S., N.L.B. and I.I.O.; writing--review and editing, N.M.I., T.T.S., N.L.B. and I.I.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Pan African Materials Institute (PAMI) under the World Bank, African Centers of Excellence (ACE) program held by the African University of Science and Technology (AUST) with the grant number AUST/PAMI/2015/5415-NG.  
Not applicable.  
The data presented in this study are available on request from the corresponding author after obtaining permission of authorized person.  
The authors declare no conflict of interest.