# Composite Foundation Settlement Prediction Based on LSTM-Transformer Model for CFG
## 4 Engineering Example  
### Engineering Overview  
This text describes a composite foundation engineering project using CFG piles for the CSLLXZQ-1 section of the newly constructed Changsha-Zhuzhou-Xiangtan intercity railway and the Chang-Shi railway connection line. The project spans from DK23 + 200 to DK23 + 350 in a test area with an average embankment height of 16 m, surrounded by complex features such as farmlands and ponds. The soil layers in the test area, from top to bottom, include subgrade soil, cushion soil, miscellaneous fill surface soil, silty clay, powdery clay, coarse sand, gravelly sand, and gravelly silty clay. CFG piles, with a diameter of 0.5 m, spaced 0.8 m apart, and an effective length of 15 m, are employed for foundation reinforcement in a square arrangement, as shown in Figure 5. The subgrade soil settlement is monitored using observation piles, while the settlement of the CFG pile composite foundation is observed through settlement plates. Four sections were chosen for the subgrade settlement observation piles, labeled as DK23 + 200, DK23 + 250, DK23 + 300, and DK23 + 350. Each section has three settlement observation piles, named according to the section mileage + G1 (G2, G3). Additionally, settlement plates are positioned at four locations along the observation sections, named according to the section mileage + L1. As illustrated in Figure 6 below, one most representative observation points is selected for predictive analysis in each section. As shown in Figure 7, Figure 7a is an onsite installation  
The significant variation in the ground settlement rate during both the construction and post-construction periods of this high-fill CFG pile composite foundation made it challenging to maintain a consistent monitoring frequency. Consequently, the data obtained from on-site monitoring was non-uniform in timing, rendering it unsuitable for temporal analysis. To meet the requirements for temporal analysis, the original monitoring data underwent equidistant processing using a cubic spline interpolation method. Considering the precision and efficiency of neural network training, the data was processed into equidistant settlement records at intervals of 24 h, comprising a total of 160 observation points. The resulting interpolated ground settlement data is illustrated in the following Figure 9.  
From Figure 9, it can be observed that the settlement curves of the four typical monitoring sections exhibit a distinctive'step' shape over time. Specifically, as the embankment height increases, the settlement of the composite foundation also increases. As the current stage of embankment construction is completed, the rate of increase in settlement of the composite foundation slows down. Additionally, it can be noted that the settlement rates of the four typical monitoring sections show a similar pattern. In the early stages of embankment construction, the consolidation rate of the composite foundation soil is relatively fast. As the upper load increases, the composite foundation soil gradually densifies, resulting in a gradual slowing of the settlement rate, ultimately reaching a stable state. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Soil Layer Name** & **Thickness/m** & **Density** & **Elastic Modulus** & **Poisson’s Ratio** & **Friction Angle/\({}^{\circ}\)** & **Cohesion /kPa** \\ \hline subgrade soil & 16 & 2100 & 80 & 0.25 & 15 & 25 \\ subbase soil & 0.4 & 1900 & 46 & 0.3 & 41 & - \\ miscellaneous fill & 2.8 & 1530 & 15 & 0.33 & 15 & 20 \\ surface soil & 3.3 & 1830 & 6 & 0.45 & 10 & 13 \\ silty clay soil & 4.6 & 1650 & 35 & 0.4 & 18 & 30 \\ coarse sand & 2.1 & 1910 & 50 & 0.34 & 33 & - \\ gravelly sand & 2.0 & 1750 & 55 & 0.3 & 36 & - \\ gravelly silty clay & 15.6 & 1860 & 40 & 0.4 & 20 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Soil parameters table. Figure 8: Schematic diagram of typical section geometry. The magnitude range of input and output values significantly influences the predictive performance of the model mentioned above.