# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 1 Introduction
Currently, BIM is primarily used for design and construction, offering benefits such as 3D visualization, clash detection, and improved coordination among stakeholders [34]. Some projects have adopted 4D (time) and 5D (cost) BIM for scheduling and cost estimation, but the lack of real-time data integration limits its full potential [35]. Unlike above-ground structures, underground environments present unique challenges, including difficulty in deploying Internet of Things (IoT) sensors for real-time monitoring, the absence of standardized frameworks for merging BIM with DTs, and the computational demands of processing large-scale geotechnical and sensor data [36]. There has been an effort to design optimization and simulation of Tunnel Boring Machines (TBMs) by integrating DT with Virtual Reality (VR) to streamline the assembly process and performance evaluation of Tunnel Boring Machine (TBM) tunnelling operations [37; 38]. Recent research has introduced a digital twin-driven blast-induced vibration optimization method aimed at enhancing blasting operation [39]. A digital twin-enabled decision support method for the proactive maintenance of Tunnel Electro-Mechanical Equipment (TEE), integrating BIM, IoT, and Semantic Web technologies to enhance real-time fault detection and predictive maintenance was developed [40]. However, typical large underground cavern construction projects that use drill-and-blast tunnelling methods still need attention. With the current advancements in real-time sensor networks, AI-driven analytics, and high-performance computing, there is great potential for adopting DT in underground cavern construction [41; 42]. It can provide significant benefits for optimizing the construction and stability of underground structures [43]. One major advantage is the ability to develop a virtual model that simulates rock mass behavior under different conditions, offering valuable insights into potential risks and challenges during construction and operation [44]. Additionally, continuously updating the real-time information from the construction site enables proactive decision making and risk management, helping to prevent unexpected failures or project delays [45]. Despite the increasing adoption of BIM in underground construction, its integration with DT technology remains largely unexplored [46]. Existing research has primarily focused on BIM applications in design and construction, with limited studies addressing its transition into an operational digital twin capable of continuous monitoring, analysis, and optimization. Furthermore, the lack of standardized methodologies for integrating sensor networks, geotechnical monitoring, and real-time performance data with BIM models presents a significant challenge. Additionally, underground construction has fewer case studies demonstrating successful BIM-enabled DT, further slowing adoption. Addressing these limitations is crucial to advancing underground construction towards data-driven, real-time decision making. Furthermore, the introduction of Industry Foundation Classes (IFC) standards in underground engineering has been under the process of development for data exchange and improving interoperability [47; 48; 49]. It is necessary to explore the potential of DT integration and key challenges for a structured approach for seamless data connectivity in underground cavern construction. This research study develops a novel DT-driven framework that provides real-time feedback from the real world to a virtual BIM-based environment, enabling real-time decision making for optimizing stability assessment and rock support strategies. The proposed approach follows a seven-step DT-driven framework to integrate BIM, sensor-driven feedback loops, and predictive modeling, enabling real-time excavation progress tracking, as-built information management, and convergence monitoring based on an IFC-based data structure.