## 1 Introduction
In recent years, various artificial intelligence techniques have been applied to predict OMC, MDD, and UCS of natural and stabilized soils. For example, [PERSON] et al. [8] adopted support vector machine (SVM) and three different types of ANN, which are Bayesian regularization method (BRNN), Levenberg-Marquardt algorithm (LMNN) and differential evolution algorithm (DENN) to predict the MDD and UCS of cement stabilized soil. Based on the statistical performance measure, the authors claimed that SVM outperforms ANN models. [PERSON] et al. [9] proposed artificial neural networks for the prediction of MDD and OMC for stabilised soil. Multilayer perceptron (MLP) architecture was adopted to develop the ANN model. The results of the tests confirm that the proposed models are satisfactory. [PERSON] et al. [10] developed AI models to determine the MDD and the UCS of cement stabilized soil. The employed algorithms were functional networks (FN) and multivariate adaptive regression splines (MARS) and compared their performance with four models presented in [8], which are BRNN, LMNN, DENN, and SVM. Based on statistical performance measurement, the authors concluded that the adopted algorithms perform better compared to SVM and ANN based models. [PERSON] et al. [3] attempted to predict OMC and MDD of lime-stabilized clayey soils using ANN. The authors claimed that the developed ANN model can be effectively utilized to predict MDD and OMC properties of stabilized clayey soils. [PERSON] et al. [11] also developed a model to predict the UCS of soils using ANN. Based on the performance evaluation, the authors concluded that ANN can accurately predict UCS. ANN and adaptive neuro fuzzy inference system (ANFIS) have been applied to predict UCS of compacted soils by [PERSON] et al. [12]. They compared the performance of both models and found that the ANFIS model is superior to the ANN. [PERSON] and [PERSON] [13] also adopted ANFIS to predict the UCS of stabilized soil. The authors compared the prediction performance of ANFIS with nonlinear regression (NLR) and claimed that ANFIS outperforms NLR. [PERSON] and [PERSON] [14] reported that ANN can be utilized for predicting the UCS fiber-reinforced cement stabilized fly ash mixesaccurately. They also compared its performance with the conventional multiple linear regression model (MLR) and concluded that ANN is superior to MLR. The earlier works' attempt to predict OMC, MDD, and UCS of stabilized soils using AI approaches are encouraging. Though several types of AI algorithms can solve complex nonlinear regression problems efficiently, most of the works mainly utilized ANN. Indeed, ANN is one of the commonly applied approaches to solve several civil engineering problems, and some practical examples can be found in [15; 16; 17; 18]. However, ANN has several limitations. For instance, a reasonable interpretation of the overall structure of the network is often challenging, and they do not provide information about the relative importance of the predictors. ANN is also not always superior to other AI models to solve problems, and it is impossible to comprehend which algorithm would surpass for a given problem all the time. As a show case, the work of [PERSON] et al. [8] demonstrated that SVM outperforms ANN models in predicting the MDD and UCS of cement-stabilized soil. In addition, the majority of the previous works employed a low number of data obtained mainly from a single experimental study. In this work, models based on an ensemble of regression trees are developed to predict OMC, MDD, and UCS of stabilized soils. The main contribution of this work is the development of OMC, MDD, and UCS prediction models using ensemble methods and employing a wide range of stabilized soils acquired from different countries around the globe. The structure of the remainder of the paper is as follows: In Section 2, the fundamental principle about regression tree ensembles is presented. In this section, the most commonly applied methods to form ensembles of regression trees are discussed in detail.