## 1 Introduction
During their prediction, they correlated wave velocities to the mechanical properties, meaning that they used three types of ultrasonic velocities as input parameters. The SVM models resulted in more accurate results due to the over-fitting issues observed in ANN models [34]. In [PERSON]'s work, the prediction of the mechanical properties of light weight aggregate concrete (LWAC) was examined through the use of SVM and finite elements (FEM) models. Both models efficiently predicted the mechanical properties of the LWAC but the SVM displayed slightly better performance with lower average error [35]. [PERSON] et al. (2013) investigated the important parameters to be considered when using the SVM models as they control the tradeoff between under-fitting and over-fitting. They emphasized the advantage of using SVM models for small sizes of sample sets. Hence, efficient learning from a limited number of samples that is very important in shortening the material's developments cycle [36]. [PERSON]'s (2020) study used six different ML algorithms, among which was SVM, to develop a hybrid technique in estimating the compressive strength of jet grouting composite. They compared the different techniques based on their accuracy and concluded that SVM models performed better [28]. The work carried out by [PERSON] et al. (2020) utilized multivariate models, namely linear regression, nonlinear regression and mixed models. The multivariate models were used to predict the compressive strength of lateritic soil stabilized with agro-waste. They selected three independent variables to elucidate their effect on the compressive strength. They concluded that the linear models performed better than nonlinear; meanwhile, the mixed models performed better than the two previous [37]. [PERSON] (2013) used regression to model the compressive strength of expanded polystyrene concrete. Theyfound that the regression model can be ideally used to assess the durability of the expanded polystyrene concrete [38]. The simple logistic regression is a common classification technique and is a useful method for solving the binary classification problem. Another category of classification is multinomial classification, which handles the issues where multiple classes are present in the target variable. This study intends to develop ANN, SVM and LR models to predict the mechanical behavior of alkali-activated termite soil bricks. In this study, six input characteristics, namely Si/Al, initial curing temperature, activator concentration, water absorption, curing regime and weight, were used to predict the compressive strength. Si/Al in alkali-activated materials is a key component that controls the particles binding, and thus commands mechanical behavior (macrostructure). The initial curing temperature controls the period of the chemical reactions taking place, while the curing regime determines the route of the chemical reactions during the curing period. Water absorption partially determines the dimensional integrity of the alkali-activated termite soil (AATS). Additionally, the variation of these characteristics facilitates the prediction of the optimal compressive strength. ANN, SVM and LR models can be divided into training, validation, and testing. The training set is used to train the models. Validation data provide an evaluation of the models' fit on the training data to prevent the models over fitting and also to stop the training when the error increases. The models are finally applied on the experimental data to predict the compressive strength of the alkaline-activated termite soil (AATS). The subsequent sections of this investigation are organized as follows: Section 1 provides a brief review of the different literature using ML algorithms to predict the behavior of construction materials plus the knowledge contribution of this paper. In Section 2, the materials used and experimental program are presented with a brief description of the models used in this investigation.