Site characterization relies on both in-situ and laboratory testing. In the early stages of a project, in-situ tests are often performed before launching a full laboratory testing program. At this stage—when soil data is limited—in-situ tests can provide valuable insights for preliminary characterization. To enhance the interpretation of these tests, an automated parameter determination framework has been developed, employing a graph-based approach to derive soil and constitutive model parameters from in-situ measurements. Several studies have been conducted to validate the framework’s output in terms of both soil properties and model parameters. The framework is designed to be transparent and adaptable, allowing users to trace the computed values for different parameters and incorporate their experience, knowledge and expertise. In this study, the tool was applied to a well-documented test site in Australia. Additionally, the integration of machine learning models for predicting soil parameters is explored as part of ongoing efforts to incorporate data-driven techniques into the framework.

In-situ testing has numerous applications in geotechnical engineering. The interpretation of in-situ test results includes soil stratification and determination of soil parameters. This paper presents an automated parameter determination framework that aims to determine constitutive model parameters based on in-situ tests. The ongoing research project relies on a graph-based approach for determining the parameters. The framework has two main attributes: transparency and adaptability. Transparency is achieved by illustrating how a certain parameter was computed. Adaptability is ensured by allowing users to incorporate their expertise into the framework. The system currently determines parameters based on three main workflows that utilize the results of cone penetration tests, dilatometer tests, and shear wave velocity measurements. This study employs the three main workflows to determine soil parameters for one of the Norwegian GeoTest Sites. Additionally, the connection between the parameter determination system and finite element analysis is discussed, where the parameters for the Modified Cam Clay model are evaluated. The framework is valuable in the early stages of projects, providing detailed soil information when soil data is limited. Ongoing research aims to assess the accuracy of the derived soil and constitutive model parameters and to expand the system’s capabilities by including additional in-situ tests.