MODELING METHOD ON CYCLIC SHEAR BEHAVIOR OF SOIL BASED ON A COMBINATION OF DEEP LEARNING AND MATHEMATICAL MODELS

Abstract

Mathematical models are widely used to represent the cyclic shear behavior of soil based on cyclic shear test data. These models, however, cannot accurately trace all test results. In this study, the researchers de- velop a new model that combines the advantages of both a deep learning model that can accurately repro- duce test data and mathematical models that robustly represent unknown behaviors. This combined model shows not only an improvement in the prediction performance for shear stiffness at increased levels of strains, but also more robustly represents unknown behaviors by virtue of following mathematical models.