## 6 Case Study  
Section 5 outlined the CPT stratification and the determination of constitutive model parameters for different layers. This section demonstrates the full potential of APD through a synthetic case study. The APD workflow is illustrated in Figure 8, beginning with in situ raw measurements. These measurements are first imported into APD, followed by stratification (module 2). Subsequently, soil and constitutive model parameters are evaluated using the graph-based approach (modules 4 and 5). An automatic connection to PLAXIS (version 24.2) [78] is then established, where a borehole is created with the corresponding values, and the constitutive model parameters for each layer are automatically assigned, enabling the user to proceed with the numerical analysis. On a 3.70 GHz CPU, the entire process--from raw data input to borehole definition in PLAXIS--takes approximately 5 s.  
Currently, APD processes one CPT at a time. Ongoing research aims to extend the system to handle multiple CPTs simultaneously. This enhancement will enable the generation of more complex subsurface models with inclined or spatially varying layers when transferred to the finite element software. \begin{table}
\begin{tabular}{l l l l l l} \hline  & **Layer 1** & **Layer 2** & **Layer 3** & **Layer 4** & **Layer 5** \\ \hline Model & HS & HSS & HSS & HSS & HSS \\ \(SBT_{n}\) & Silty sand and sandy silt & Clay & Clay & Sensitive fine grained & Sensitive fine grained \\ Drainage & Drained & Undrained B & Undrained B & Undrained B & Undrained B \\ \(\gamma_{l}\) (kN/m\({}^{3}\) ) & 18.83 & 15.91 & 14.96 & 14.59 & 15.47 \\ \(E_{50}^{ref}\) (kPa) & 34,400 & 7761 & 3461 & 3780 & 5638 \\ \(E_{eq}^{ref}\) (kPa) & 34,400 & 6209 & 2769 & 3024 & 4511 \\ \(E_{ur}^{ref}\) (kPa) & 137,600 & 38,810 & 17,300 & 18,900 & 28,190 \\ \(v_{ur}\) (-) & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 \\ \(m\) (-) & 0.7 & 1 & 1 & 1 & 1 \\ \(G_{0}^{ref}\) (kPa) & - & 47,620 & 37,260 & 34,700 & 54,300 \\ \(\gamma_{0}\) (-) & - & 2.09 \(\times 10^{-4}\) & 2.55 \(\times 10^{-4}\) & 1.74 \(\times 10^{-4}\) & 2.10 \(\times 10^{-4}\) \\ \(\phi^{\prime}\) (\({}^{\circ}\)) & 37.77 & - & - & - & - \\ \(s_{u}\) (kPa) & - & 20.32 & 16.42 & 25.85 & 46.92 \\ \(K_{0}\) (-) & 0.8414 & 0.8601 & 0.6189 & 0.6383 & 0.6834 \\ OCR (-) & 3 & 2.951 & 1.453 & 1.306 & 1.539 \\ \hline \end{tabular}
\end{table} 
Table 7: Material sets for the 5 layers. To illustrate the stages outlined in Figure 8, a synthetic case study involving a shallow foundation is modelled. The finite element (FE) axisymmetric model is shown in Figure 9, where a circular footing with a radius of 0.9 m is placed on layer 2 (Clay 1). The footing has a height of 0.6 m, with a 0.3 m gap between the footing and layer 1. The groundwater table is positioned 1.45 m below the ground surface. The FE model consists of 33,049 15-noded elements, extending to a depth of 23.10 m and a width of 12 m. Point 1 used for plotting the results, is located directly beneath the footing, as highlighted in the figure. The calculation sequence followed these steps:  
* Initial stress generation
* Excavation
* Foundation installation
* Loading  
The footing was modelled as a linear elastic material representing concrete, with a unit weight of \(\gamma=24\) kN/m\({}^{3}\). To ensure the stability of layer 1 during excavation, a thin cluster (20 cm wide) was introduced on the left side of the layer. This cluster was assigned the same properties as layer 1, with an additional cohesion of 6 kPa. The failure mechanism predicted by the finite element analysis (FEA), along with the load-settlement behaviour at Point 1, is presented in Figure 10. The failure load was determined to be 118 kN/m/m. Using APD in combination with parametrised finite element models enables an almost fully automated process for parameter identification, model generation, and calculation phase definition. By streamlining the transition from in situ test data to numerical modelling, APD reduces manual input and potential errors while ensuring consistency in parameter selection.