# Predictive Models for Correlation of Compaction Characteristics of Weakly Cohesive Soils
## 2 Predictive Models for Soil Compaction  
Predictive models for soil compaction in the context of digitalization integrate advanced data analytics, machine learning, and sensor technologies to forecast the compaction behavior of soils under various conditions. Predictive modeling for this soil compaction uses historical data, machine learning algorithms, and statistical techniques to predict future outcomes based on input variables. In soil compaction, these variables may include soil properties (such as composition, moisture content, density, and grain size distribution), environmental conditions (such as temperature, humidity, and weather conditions at the time of compaction), and technological equipment parameters (such as type of compactor, operational speed, and compaction technique). To build an effective predictive model, data must be collected, including sensor data (using smart compaction equipment equipped with sensors can provide real-time data on parameters like soil density, moisture, and temperature), historical data (previous compaction projects can yield valuable insights into soil behavior under different conditions), and environmental monitoring (e.g., weather data and site conditions can be integrated into the model to account for external influences). To develop predictive models for soil compaction, various machine learning techniques can be applied [26]: regression models, classification algorithms, and neural networks. The predictive model needs to be trained using a portion of the collected data (training set) and validated with a separate dataset (validation set). This process includes feature selection through identifying the most relevant input variables that influence compaction outcomes, and model evaluation through assessing the model's performance using metrics such as mean absolute error (MAE), root mean square error (RMSE), and R-squared values [27; 28; 29]. Once validated, the predictive model can be implemented on site to provide real-time predictions and insights for adaptive compaction control and/or forecasting compaction quality. In addition, the predictive model can be integrated with digital twin technology, which creates a virtual representation of the physical construction site, allowing for scenario simulation and continuous learning with new data, improving its accuracy over time [30; 31]. Thus, the inter-parametric laws for evaluating soil compaction performance refer to mathematical models that relate multiple parameters affecting soil compaction efficiency. Thus, these models help predict the compacted state of soil based on main factors such as moisture content, density, compaction energy, soil type, and particle distribution. Compaction evaluation involves detailed parametric monitoring of soil state by modeling the transitions through viscous-elastic-plastic stages, using rheological models to understand the compaction dynamics more accurately. The use of complex rheological models that incorporate elastic, viscous, and plastic components allows for a more granular observation of the material's behavior as it undergoes compaction. Starting from simple two-element models and scaling up to multi-element systems, this setup helps distinguish each stage of compaction and track how forces transfer through the material's layers, especially as it approaches a \"saturated\" compacted state. Once the material reaches the threshold value where the volume remains constant under additional vibration, this is an indicator of optimal compaction and stability. This threshold marks a transition where the material behaves as an \"elastic membrane\", indicating minimal internal porosity and a Poisson's ratio near 0.42-0.48. At this point, the material reaches maximum carrying capacity, signified by high dynamic stiffness, volume modulus, and internal resistance. These values ensure the layer's sustainable structural integrity, contributing to the longevity of the road structure or civil foundation.