## 1 Introduction
As a soil stabilizer, alum sludge has been shown to have varying efficiency, depending on the type of soil and the conditions of its application. As an example, clay soils are naturally active, so the use of alum sludge may greatly benefit them [22]. Similarly, the effectiveness of alum sludge as a soil stabilizer can be affected by environmental factors such as temperature, humidity, and rainfall [23]. Studies have shown that it is important to consider the specific soil and environmental conditions when using alum sludge as a soil stabilizer [23; 24]. Various factors have been identified as influencing the behavior of sludge. There are several parameters to consider, including the soil and sludge density, the specific gravity of the soil, the liquid limit and plasticity of the soil, the sludge content, etc. With such a multiplicity of parameters and nonlinearity, it would be necessary to predict performance using an equation with a large number of parameters, that would require quantification, which does not exist. In the past two decades, artificial intelligence (AI) methods have been used to resolve this issue. Using artificial intelligence techniques, it is possible to determine the relationship between different parameters with a high degree of accuracy, without prior knowledge. Various topics in geotechnical engineering, such as slope stability [25; 26; 27], tunneling [28; 29; 30], pavement and road construction [31; 32], soil cracking [33; 34; 35], rock mechanics [36; 37], soil dynamics [38; 39; 40; 41], and soil stabilizers [42; 43; 44] have been addressed using artificial intelligence methods [45]. Nevertheless, only two studies have used artificial intelligence to predict the properties arising from mixing sludge with soil [46; 47]. [PERSON] et al. [46] used a small database of 18 datasets to predict the CBR parameter, using an artificial neural network (ANN). A high \(R^{2}\) of 0.99 was observed, as well as a small RMSE of 0.057. Similarly, [PERSON] et al. [47] used a 21-set database and artificial neural network (ANN) method to predict the CBR of mixtures of sludge and soil. The results of the study were very promising, with an \(R^{2}\) of 0.97 and an RMSE of 0.58. However, both studies used only one classic artificial intelligence method, ANN, and grey box methods were not employed. Grey box methods are those methods that have an output such as a tree or an equation. In addition, the used databases were relatively small, which may limit the range of values of the parameters. In order to fill these gaps, this study uses two black-box AI methods, namely an artificial neural network (ANN) and support vector machines (SVM), as well as a grey-box AI method, namely genetic programming (GP), in order to predict CBR. By using this approach, both black-box and grey-box models will be examined. The ANN method that is currently used as standard, will be compared with another black-box method, SVM. SVM and GP have been widely used in the field of civil engineering for various applications. For example, SVM has been used to predict the remaining service life of aging infrastructure, such as bridges and buildings [48; 49; 50]. SVM has also been used for damage detection and identification in structures, using vibration data [51; 52]. SVM has found application in predicting the degradation of pavements and estimating their remaining service life [53; 54]. Additionally, SVM has been utilized for classifying the types of distresses on pavement surfaces and forecasting their friction coefficient [55]. GP has been utilized for optimizing the operation and design of various systems in civil engineering, including water supply [56] and stormwater management systems [57], and for analyzing flood forecasting and dam failure [58]. Furthermore, GP has been applied to optimize the design of various structures, including trusses [59], frames [60], and bridges [61], as well as for the placement of sensors to monitor the structural health of buildings [62].