# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 1 Introduction  
Nowadays, filling materials of high quality are becoming rare in the infrastructure engineering construction process. With a sustainable concept and solution, more types of soils are considered as filling materials. Some soils that were previously not allowed to be used as road construction materials now can be utilized after testing and evaluation, which achieve waste resource recycling and utilization. Compaction performance is a vital property to determine whether a kind of soil can be used as a filling for roads or other engineering. The density of soils can be increased by increasing the contact area between particles and decreasing the distance between particles when subjected to an external force [1; 2]. In geotechnical engineering, the compaction can affect the stability, deformation characteristics, and bearing capacity of soil [3; 4]. By controlling the compaction state of soil, the strength and stability of soil can be improved, and deformation can be reduced, which ensures the safety and reliability of geotechnical engineering. Therefore, in engineering construction, it is crucial to control the compaction of soil reasonably. Evaluating the compaction of soils can usually be accomplished by determining the maximum dry density (MDD) and optimum moisture content (OMC) [5]. The standard and modified Proctor test is the most commonly used compaction test for determiningOMC and MDD. In the Proctor compaction test, the dry density of the soil when it reaches the tightest state under a certain number of strikes is MDD, while the moisture content of the soil at the same state is OMC. These parameters are important for construction facilities such as highways, bridges, and houses. For example, [PERSON] et al. [6] used OMC and MDD to evaluate the compaction characteristics of cured lead-contaminated red soil; [PERSON] et al. [7] used OMC and MDD to study the engineering properties of roadbed soils; and [PERSON] et al. [8] investigated the microstructure and hydraulic properties of roadbed soils using OMC and MDD. The accurate prediction of OMC and MDD can improve the design accuracy, construction quality, and economic efficiency of geotechnical engineering. The Proctor compaction test can be used to predict OMC and MDD [9], but it is a time-consuming and labor-intensive method that requires multiple tests to plot compaction curves [10]. Although this method can provide reasonable predictions in many cases, it may be subject to a certain amount of error because it is limited by experimental conditions and sample size. Regression analysis is a statistical method for calculating soil compaction parameters and can be used to predict soil compaction parameters [11; 12; 13; 14; 15]. However, this method can only make preliminary predictions of soil MDD and OMC and has a large error [16; 17; 18; 19; 20]. Multiple linear regression allows the creation of empirical equations to predict the OMC and MDD of soils [21; 22]. Empirical equations may affect the reliability of prediction due to the presence of multiple factors, and empirical equations can only reflect linear soil behavior, not complex nonlinear soil behavior. Therefore, this requires researchers to develop more advanced methods to predict OMC and MDD. In recent years, with the increasing development of artificial intelligence technology, some researchers have utilized this technology to predict the compaction parameters of soils, which have been proved to have good performance and advantages. [PERSON] et al. [23] established a prediction model for the compaction characteristics of fine-grained soil through logistic regression analysis. [PERSON] et al. [24] applied Support Vector Machine (SVM) to develop a model for predicting the compaction parameters of soil. [PERSON] et al.