# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.1 Real-Time Field Monitoring and AI Methods  
Current real-time monitoring systems for open caissons have shown remarkable progress, providing instantaneous visualization of the sinking process; however, they still fall short of adequately assisting in construction decision-making. This is largely due to the complexity and heterogeneity of the foundation soils and rock formations, which makes it challenging for open caissons to consistently maintain uniform descent during sinking operations. Thus, there is a critical need for timely adjustments and corrections to prevent deviation. This implied a necessity for prompt decision-making regarding the construction status throughout the process. ACPP is an actively controlled technology, where sinking and excavation operations are automatically controlled by the adopting \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{2}{l}{**DEM vs. FEM Simulations**} & & & \\ \hline Parametric Aspect & DEM & FEM & Discrepancy & Causes \\ \hline Failure Mechanism & Captures discrete particle movement and arching & Continuum deformation field & DEM better captures localized failure & DEM reflects granular behavior better; FEM assumes continuous material \\ \hline Computational Cost & High (large number of particles) & Moderate (mesh-dependent) & - & DEM requires larger resources for realistic scales \\ \hline \multicolumn{2}{l}{**1-g Physical Model Tests vs. Centrifuge Modeling**} & & & \\ \hline Parametric Aspect & 1-g Model & Centrifuge Model & Discrepancy & Causes \\ \hline Stress Condition & Prototype stress level & Correct in situ stress level achieved & 15-25\% lower settlements in 1-g & Insufficient confining stress; scaling issues \\ \hline Failure Pattern & Incomplete development & Clear settlement trough and wall deformation & - & Dissimilarity in Stress and boundary conditions \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._(AI) system employing the monitoring sensors data, shown in Figure 15. So far, the ACPP technology was recently used for the first time on the west expansion of Shanghai Metro Line 13 to construct the escape shaft project at Yunle Road Station, demonstrating the effectiveness of this cutting-edge innovation. Measurements gathered throughout the project yielded remarkable results. The inclination of the sinking well achieved 0.65% precision, with an ultimate soil settlement of 4.95 mm and a corner height variability of only 2 mm. Additionally, the segment lining demonstrated no evidence of leakage or incorrect alignment, reflecting great overall reliability [172]. Advancements in information technologies and artificial intelligence technologies have enabled the application of data-driven approaches in auxiliary decision-making in civil engineering [174]. Using the convolutional neural network, [PERSON] et al. [175] studied the stress characteristics of an open caisson structure, which can effectively predict the sinking speed of the open caisson to provide a basis for safe construction. The soil pressure data from foot blades can accurately depict the stress level during sinking. The excavation scheme was designed based on the stress level at various stages of sinking [176]. By comparison of machine learning models, [PERSON] et al. [177] revealed that the multilabel K-nearest neighbor (MLKNN) was the best successful model predicting open caisson excavation instructions. Improvements to MLKNN, such as enhanced normalization and distance measures, increased its accuracy to 89.04% for excavation predictions and 98.63% for non-excavation decisions. However, these data-driven methods for open caisson excavation were highly accurate in the middle and later stages of construction, but they were less effective in the initial phase. Addressing this constraint required additional study to improve algorithmic decision-making at the early stages, when uncertainties were the largest.