###### Abstract  
Cement stabilized soil is one of the commonly used as ground reinforcement solutions in geotechnical engineering. In this study, the main object was to apply three machine learning (ML) methods namely gradient boosting (GB), artificial neural network (ANN) and support vector machine (SVM) to predict unconfined compressive strength (UCS) of cement stabilized soil. Soil samples were collected at Hai Duong city, Vietnam. A total of 216 soil-cement samples were mixed in the laboratory and compressed to determine the UCS. This data set is divided into two parts of the training data set (80%) and testing set (20%) to build and test the model, respectively. To verify the performance of ML model, various criteria named correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used. The results show that all three ML models were effective methods to predict the UCS of cement-stabilized soil. Amongst three model used in this study, optimized ANN model provided superior performance compare to two others models with performance indicator R = 0.925, RMSE = 419.82 and MAE = 292.2 for testing part. This study can provide an effective tool to quickly predict the UCS of cement stabilized soil with high accuracy. gradient boosting; artificial neural network; support vector machine; feature important analysis +
Footnote †: 2021: 11, 1949. [[https://doi.org/10.3390/app11041949](https://doi.org/10.3390/app11041949)]([https://doi.org/10.3390/app11041949](https://doi.org/10.3390/app11041949))
Based on the experimental results, [PERSON] et al. [15] gave the experimental equations to calculate UCS of high water content cement stabilized clay based on the ratio of cement and water. In addition, [PERSON] et al. [16] have developed a standardized experimental model to predict cement stabilized soil strength based on [PERSON]'s law, and at the same time consider the cement-water ratio as the main parameter. Last but not least, a model equation to calculate the cement content required to reach a desired strength, which scientist [PERSON] et al. [17] have drawn from the results of experiments on soft clay mixed cement and considering the ratio of water-cement is a microstructural parameter. Through many studies it can be found that the UCS value is an important parameter to evaluate the bearing capacity of cement-stabilized soil [3; 9]. However, the UCS value is mainly determined by experimental studies and experimental equations. However, the experimental method often requires a large number of samples, it is expensive and time-consuming [18]. Moreover, the experimental equations often have to be based on approximate hypotheses, so errors still exist [16]. In addition, the soil properties in each place are so different, it is difficult to apply the general experimental formulas. In recent years, artificial intelligence and machine learning models are used more and more widely, many scientists in the field of geology apply these models to predict the UCS value of soil stabilized with cement. Of particular note is the study of [PERSON] et al. [19]. They built a genetic programming model GP to predict the UCS value of red earth (CL), brown earth (CH) and black cotton soil (CH) stabilized with cement. In addition, the algorithms ANN and SVM are also used to predict unconfined compressive strength of cement stabilized soil [18; 20] these models show high accuracy, and cost savings compared to experimental methods [18]. In this study, the main object is to apply three machine learning (ML) methods namely gradient boosting (GB), artificial neural network (ANN) and support vector machine (SVM) to predict unconfined compressive strength of cement stabilized soil. The model architectures were optimized then using the Monte-Carlo method to model and consider the randomness of the data division. To verify the performance of the ML model, different criteria named correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used.