## 1 Introduction  
Stabilized base or subbase materials are a mixture of aggregates, water, and cementitious materials and/or emulsified asphalt. The use of stabilized materials in the construction of bases can reduce the occurrence of failure-related cracking (i.e., fatigue cracking at the bottom of the asphalt layer) owing to the relative higher stiffness of these materials compared with conventional materials [1]. However, cracks (i.e., reflective cracks) that occur in the asphalt layer are usually due to a source of cracking that may be present in a stabilized base layer. The proper design and construction of a stabilized base layer can last through asphalt maintenance and/or asphalt overlays, or a stabilized subbase layer can be used instead underneath a conventional base layer [2]. Stabilized materials should be sound and durable enough to resist the traffic load and changes in climate; in particular, Wet-Dry (W-D) and freeze-thaw effects [3]. According to the Mechanistic-Empirical Pavement Design Guide, among other factors, W-D and freeze-thaw cycles are important parameters that degrade the base/subbase materials and may contribute to premature failure of pavements [4]. There is a significant correlation between the W-D and freeze-thaw conditionsin terms of durability and the resilient modulus (M\({}_{\rm r}\)) or equivalent elastic modulus, which measures the performance of base materials in a pavement structure [4; 5; 6; 7]. M\({}_{\rm r}\) values can be measured in the laboratory in accordance with the AASHTO T307 [8] or predicted by soft computing models [3; 9]. A number of studies, e.g., [PERSON] et al. [10] and [PERSON] and [PERSON] [7], have been conducted to measure the influence of W-D cycles on the resilient modulus of stabilized base materials in the laboratory. They found that the addition of cementitious additive to base materials increased the durability of the stabilized specimens against W-D cycles, and hence increased the resilient modulus. On the other hand, [PERSON] [1] and [PERSON] and [PERSON] [11] recommended a regression model for predicting the M\({}_{\rm r}\) of stabilized base aggregates based on the number of W-D cycles, ratio of oxide compounds in the cementitious materials (CSAFR), physical properties of the mixture, and stress levels. This model is described as M\({}_{\rm r}\) = \(f\) (W-D, CSAFR, DMR, \(\sigma_{3}\), \(\sigma_{d}\)), where CSAFR is the amount of free lime, silica, alumina and ferric oxide compounds in the cementitious materials, DMR is the ratio of maximum dry density to the optimum moisture content, and the attributes, \(\sigma_{3}\) and \(\sigma_{d}\) are the confining pressure and the deviator stress, respectively. [PERSON] et al. [4] used Support Vector Regression (SVR) to model the M\({}_{\rm r}\) of stabilized base aggregates subjected to W-D cycles, and they found that the SVR prediction model outperformed the regression and least square methods. Recently, highly advanced learning algorithms were introduced for the modeling of engineering applications. Among these methods, Artificial Neural Network (ANN), the Particle Swarm Optimization algorithm (PSO), and the Extreme Learning Machine (ELM) are highly common methods utilized in designing prediction models. ANN has been widely used to predict the M\({}_{\rm r}\) values for pavement materials. For instance, [PERSON] and [PERSON] [12] utilized the ANN to predict the M\({}_{\rm r}\) for stabilized base aggregates, which were compared with those of the SVR model. They concluded that the ANN was superior to the SVR model for predicting the M\({}_{\rm r}\) values of stabilized bases. [PERSON] [13] used ANN to model the M\({}_{\rm r}\) values of recycled concrete aggregates for the construction of bases and subbases. They found that the ANN model could be used to predict an accurate M\({}_{\rm r}\) for recycled materials. [PERSON] et al. [14] found that the ANN model was able to correlate M\({}_{\rm r}\) with routine properties and stress states for subgrade soils.