## 5 Connection To Numerical Modelling
### Stratification  
Several stratification methods implemented in APD are based on soil behaviour type (SBT) charts, which are determined using one of three commonly used charts: [PERSON]'s normalised SBT chart [27], modified non-normalised SBT chart [50], or the updated normalised SBT chart [61].  
For each CPT measurement, the SBT is assigned using one of these classification charts. APD currently supports three distinct approaches to generating soil layers from CPT profiles. The first two are automated techniques that cluster consecutive CPT readings sharing the same SBT classification. Layer interfaces are introduced at locations where a notable shift in SBT occurs, resulting in a segmented CPT profile composed of a limited number of layers, each characterised by its average SBT. The third method is a manual approach (applied in the case study presented in Section 3.3), where the user defines the stratigraphic boundaries, and these inputs are preserved throughout the stratification process.  
Additionally, other automated stratification approaches from the literature (e.g., [62]) can be applied. Methods employing machine learning (ML) models, such as those proposed in [63], can also be incorporated into the APD framework. External tools, like the CPT interpretation software package CPeT-IT v3.0 [64], can also be utilised for stratification purposes. In general, any suitable method can be integrated into the automated system, which makes the system again very adaptable.  
In the following study, CPeT-IT (version 3.9.5.9) was used as the source for stratification. The CPTu sounding, presented in Figure 2a-c, was imported and stratified using CPeT-IT. The layer detection algorithm implemented in CPeT-IT follows the method outlined by [65], based on a simple univariate statistical analysis of the \(q_{t}\), normalised cone resistance corrected for stress level (\(Q_{tn}\)), \(l_{cn}\), or modified soil behaviour type index (\(I_{B}\)) profiles. For this study, the analysis was performed using the \(I_{cn}\) profile. The resulting stratification is shown in Figure 7. The algorithm detects soil layer boundaries based on the peaks in the T Ratio plot (Figure 7). The vertical red line in the T Ratio plot represents the threshold value for identifying significant peaks. A minimum layer thickness of 0.5 m and a window width of 30 points were set. The T Ratio plot is then generated based on the selected window width, with high values indicating significant changes in the parameter, suggesting a transition between soil layers. This approach resulted in six distinct layers, with the upper crust (top 1.45 m) identified as silty sand and sandy silt. Below this, two clay layers were detected, followed by two sensitive fine-grained layers, and a very thin sand and silty sand layer at the end of the sounding. Comparing the obtained stratification with the interpreted profile at the test site (Figure 2), the upper crust layer was accurately identified. The subsequent fine-grained layers also align well with the interpreted soil profile, whether considering the initial stratification with nine soil layers or the simplified version with three soft clay layers.