## 1 Introduction  
Over the last decade, a global shift has been noticed in the field of construction. The requirements are oriented in terms of eco-friendliness, renewability, cost, availability, reliability and sustainability for construction materials. These requirements are driven by the concern for the protection of the ecosystem, to reduce CO\({}_{2}\) emission and use wastes for repairing [1], upgrading and constructing [2]. Most of the research effort are directed towards innovating empirical and traditional materials for construction through modern and sustainable technologies; thus, there is interest in earth-based materials [2; 3; 4; 5]. Earth-based materials have long been used empirically as a construction material, among them being termite mound clay [6]. Termite mound soil (TMS) is the soil obtained from the anthill [7; 8; 9; 10; 11; 12; 13; 14; 15; 16]; it is spread abundantly around the tropics [17] but considered as waste [18; 19; 20; 21; 22; 23; 24]. Additionally, in the construction field experimental tasks are time consuming, very expensive and some properties cannot be easily modelled due to the complex relationship between the mechanical properties and the constituents. In order to minimize the experimental tasks and increase the accuracy of data, researchers focused on the use of artificial intelligence (AI) models which are similar to the human brain [25] and capable of solving very complex variables [26; 27]. Machine learning (ML) is a subset of AI used to anticipate and evaluate various properties of construction materials. Henceforth, ML approaches have gained a lot of interest in construction applications to predict the structural behavior of different elements. In addition, data processing indicated a high efficiency performance because the outputs can be predicted using the inputs without knowing their correlations [28]. ML models consist of computer algorithms capable of generating (anticipating) patterns and hypotheses through a provided dataset for future values. They have been proven to be effective in saving time, cost, their ability to satisfy the requirements of various design codes, standards [29] and future applications. ML techniques can accurately predict the behavior of materials [30] although the relationships between the input and output are nonlinear [29] or not easily modeled [31]. In the prediction of concrete behavior, artificial neural networks (ANN), support vector machines (SVM), decision trees, and evolutionary algorithms (EA) are the four models that are mainly used [29]. In [PERSON]'s et al. (2018) work, they utilized back-propagation ANN to predict the compressive strength of recycled aggregate concrete (RAC). They obtained very accurate regression values for the training, validation and testing of 0.903, 0.89 and 0.829, respectively [31]. Meanwhile, the study conducted by [PERSON] et al. (2016), utilized ANN methods to comparatively examine the compressive strength of concrete. Their study was a comparative examination of the ANN and genetic programming (GP) to ascertain and compare the accuracy of both techniques in the prediction of the compressive strength of concrete [32]. [PERSON] et al. (2018) investigated the use of natural pozolonna from Syria in concrete. They explored the strength and durability of Syrian volcanic scoria and predicted the mechanical behavior using an ANN and multiple linear regression (MLR). From the models they used, they found out that the ANN displayed higher accuracy. They concluded by highlighting the contribution of the volcanic scoria in the reduction in the concrete's permeability and its effect on durability-related properties too [33], whereas [PERSON] et al. (2016) carried out a comparative study between ANN and MLR for predicting the compressive strength of concrete containing nanosilica and copper slag [26]. [PERSON] et al. (2019) predicted concrete's strength using support vector machine (SVM) and ANN models.