## 5 Discussions
### Limitations  
Despite the advantages of integrating digital twin and knowledge graph technologies for tunnel construction risk assessment, several limitations remain in the current study. One of the primary challenges is the availability, reliability, and quality of real-time data. The accuracy and effectiveness of the digital twin model depend heavily on the completeness, resolution, and frequency of monitoring data--such as ground settlement measurements, TBM operational parameters, and structural responses. Inreal-world scenarios, issues such as sensor noise, data loss, calibration drift, and communication delays may compromise data quality. These imperfections in the sensor data can introduce uncertainty and bias into the knowledge graph reasoning process, especially when real-time decisions are required. For instance, noisy or incomplete input data may lead to the misclassification of risk levels or delayed responses to emerging hazards. Furthermore, dependencies between different data sources may amplify the effect of local failures or missing values, particularly in complex tunneling environments. Future work will focus on integrating data confidence metrics, robust error-handling mechanisms, and redundancy in sensor networks to improve the resilience of the real-time reasoning framework and reduce the impact of data-related biases. The ontology and SWRL rule base used in this study were manually constructed based on domain standards and expert knowledge specific to urban tunnel construction in Shanghai. This rule-based approach was adopted to ensure semantic clarity and to demonstrate the feasibility of integrating digital twin and knowledge graph technologies for early warning and emergency management. However, extending the system to other types of infrastructure or geographic contexts would likely require significant manual adaptation of the ontology and reasoning rules, which may limit scalability. Future research could focus on developing automated ontology generation and rule extraction mechanisms, such as through natural language processing or pattern mining techniques, to support more efficient deployment across diverse and dynamic construction environments. In addition to the limitations discussed, future work will explore the integration of dynamic learning mechanisms within the knowledge graph, enabling the system to continuously adapt its reasoning logic based on incoming data streams and evolving risk patterns. Furthermore, to validate the generalizability of the proposed framework, pilot studies in diverse urban geological settings--such as soft soil, mixed-face, or high-groundwater-level conditions--will be conducted to assess its robustness and transferability across different tunneling environments. Although the system has been applied in real-world tunnel construction scenarios and has demonstrated good practical performance, a key limitation is the lack of a quantitative evaluation of its technical metrics. Parameters such as reasoning speed, data throughput, and the accuracy of risk predictions have not yet been formally measured or benchmarked. This limits the ability to objectively compare the system's performance with traditional methods or to assess its scalability under varying data and operational loads. Future work should include comprehensive performance profiling and controlled experiments to support more rigorous validation and facilitate broader adoption. While the current implementation has been validated in a single tunnel construction project, the potential scalability of the proposed system warrants further discussion. In larger-scale or multi-site scenarios, challenges such as increased sensor density, data throughput, and the complexity of cross-site coordination may arise. The current architecture is designed with a modular and distributed structure, which provides a basis for horizontal scaling. For instance, data ingestion, semantic mapping, and reasoning tasks can be deployed across multiple nodes or edge devices to alleviate central processing bottlenecks.