## 1 Introduction  
Numerical analysis offers significant advantages over traditional methods by providing deeper insights into geotechnical stability, limit-state design, and soil-structure interaction. However, its reliability heavily depends on the accurate determination of constitutive model parameters, which are often constrained by limited soil data and engineering judgment. Prediction competitions have demonstrated that different engineers can obtain varying results for the same problem, highlighting the subjectivity involved in parameter selection. This raises the question of whether automating parameter determination could reduce variability and enhance the reliability of numerical analyses in geotechnical design [1]. Over time, soil constitutive models have become increasingly sophisticated. While these advanced models offer a more accurate representation of soil behavior, they also require a larger set of parameters, typically derived from laboratory tests such as triaxial and oedometer tests. However, such tests may not always be available for every project, posing a challenge in parameter estimation.  
In situ testing provides an alternative method for determining soil parameters, offering advantages over laboratory tests in terms of speed, cost-effectiveness, and minimal soil disturbance. However, these tests do not yield soil parameters directly. Instead, empirical correlations are commonly used to relate field measurements to specific parameters.  
Since multiple correlations often exist for the same parameter, the derived values can vary significantly, introducing uncertainty. This variability stems from the fact that most correlations are developed based on specific soil types or conditions, which can limit their broader applicability.  
An ongoing research project aims to develop an automated parameter determination (APD) framework for identifying constitutive model parameters based on in situ test data. This approach is especially useful in the early stages of a project when soil data is limited. At this stage, relatively cost-effective field tests are conducted before undertaking a full laboratory testing program. The objective is not to replace laboratory tests but to complement them, as they remain crucial for refining soil properties and constitutive model parameters in the final design phase.  
In the APD framework, parameter determination is performed using a graph-based approach that incorporates principles of graph theory [2]. The primary objective of the project is to develop a system that is both transparent and adaptable. Transparency is achieved by explicitly demonstrating how parameter values are derived from the available data, while adaptability is ensured by allowing users to incorporate their own expertise and experience into the process.  
Section 2 briefly introduces the APD framework, followed by its application in Section 3, where soil parameters for a soft clay site are evaluated. Section 4 presents the application of APD to other sites. Section 5 discusses the stratification and the determination of constitutive model parameters, while Section 6 presents the integration of the framework with the finite element software PLAXIS and showcase the numerical modelling of a synthetic case study. Finally, Section 7 presents the conclusions and outlines directions for future research.