# 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
The empiricism-constrained neural network (ECNN) was utilized by [178] for prescriptive design; this was calibrated and compared to classic empirical models using reduced-scale laboratory data. It was concluded that both methods effectively anticipate  
Figure 15: Schematic diagram of ACPP excavation system: (**a**) automatic trajectory record; (**b**) excavation interface [173]. surface and subsurface settlement troughs, but the neural network approach was more accurate in capturing the impact of caisson depth on soil settlements. AI model performance in predicting caisson settlement is heavily influenced by training needs and data quality. Robust model development requires high-quality data and suitable preprocessing procedures, like feature selection. There are pros and cons to using AI with conventional geotechnical design techniques, and the anisotropic and heterogeneous character of soil-rock media made these predictions more challenging. A selection of related features can help in reducing noise and improving the model's predictive power to improve model accuracy [179; 180]. For the development of a successful model, large and high-quality training datasets are essential. To develop viable models, geotechnical data must be handled carefully because they are commonly inadequate [181]. According to [182], in order to increase their accuracy, AI models must account for the complicated behavior of soil-rock mediums, such as discontinuities and elastic-plastic behavior. He concluded that relative to conventional methods, advanced AI algorithms can provide better prediction capabilities by modeling the non-linear and discontinuous nature of geotechnical materials. To ensure accuracy of these models, they are validated using real-world data, and to evaluate the impact of different factors on model predictions, sensitivity analysis is crucial [183]. Incorporating advanced field monitoring systems with AI techniques, such as Physics-Informed Neural Networks (PINN) and use of actively controlled prefabricated caisson technology (ACPP), can revolutionize real-time analysis and prediction of soil behavior. Although shafts and underground structures primarily involved complex soil-structure interaction problems, the application of advanced artificial intelligence (AI) techniques offered significant potential for enhancing predictive and analytical capabilities in this domain. Significant domains under this subject could include evaluating the stresses and deformations of lining, predicting vertical and horizontal deformations due to open caisson excavation, and optimizing the selection of open caisson shaft construction technique on basis of soil profile and shaft depth. Future studies should focus on the automated evaluation of the entire process which can be achieved by integrating the proposed model and monitoring system, which could offer improved accuracy in assessing soil displacements, stress distributions, and deformation patterns. As a result, more efficient and safe construction operations will be possible if their engineering application and level of intelligence are improved.