This study investigates how incorporating physical constraints can enhance the performance of machine learning models by ensuring that geotechnical drilling data predictions align with known physical conditions at the site. Machine learning-predicted soil property point cloud data has significant value for geotechnical project planning. The base model was trained on extensive borehole datasets of soil properties collected from an area of 32,133 square km covering five distinct physiographical regions. To incorporate physics-based constraints, a custom loss function was defined to penalize the model training loss whenever it violates known physical principles. Two distinct types of machine learning models—classification and regression models—are considered in this study for categorical and numerical geotechnical drilling datasets, respectively. Feature variables play a critical role in determining the accuracy of machine learning models and feature variables including location, geology, surface elevation, soil parent material, physiographical information (codes) and soil layer depth are adopted for training the machine learning models after parametric study of various feature variable combinations. Two case studies were conducted to demonstrate the effectiveness of incorporating physical constraints into machine learning models for categorical and regression datasets respectively. The study results demonstrate strong potential for applying physics-constrained machine learning models to generate reasonable estimated values across large regions, while also providing a better understanding of the historical data within the geotechnical drilling inventory.
Zhang et al. (Tue,) studied this question.