# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 1 Introduction  
Building and maintaining large underground caverns is an important part of modern infrastructure, especially for projects like hydroelectric power plants, storage facilities, and transportation tunnels [1]. These caverns face challenging conditions, such as changes in rock properties, ground stress, and water pressure, which can affect their stability and long-term safety [2]. Geological investigations play a critical role in the design of underground caverns, as they provide essential insights into the rock mass conditions that influencestability and excavation performance [3; 4]. These investigations typically involve a combination of field and laboratory techniques, such as geological mapping, core drilling, logging, geophysical surveys, and the testing of rock samples to evaluate properties like uniaxial compressive strength, tensile strength, and elastic modulus [5; 6]. Understanding features such as discontinuities, in situ stress regimes, and groundwater behavior is also vital to assessing construction feasibility and selecting suitable support systems [7; 8]. Ensuring the stability of these structures is essential because any failure can lead to serious problems, like loss of life, economic damage, and harm to the environment [9; 10]. Traditionally, engineers have used static models and regular monitoring to assess stability during the design, construction, and operation phases [11]. These methods may not detect real-time changes in rock behavior during or after construction [12]. To overcome similar problems in other infrastructure projects, new technologies like DT and BIM are being adopted [13; 14]. These tools can also improve the accuracy and efficiency of stability management in underground projects [15]. Digital twin technology, which involves creating a dynamic virtual model of a physical system continuously updated with real-time data, has emerged as a powerful tool in recent years [16; 17]. It holds significant potential for enhancing decision making and predictive analysis across various engineering disciplines [18; 19]. Recently, there has been a lot of effort in the use of DT in civil engineering and its application across a range of infrastructure projects, such as bridges, buildings, and transportation systems [20; 21]. It plays a crucial role in managing construction projects by creating a dynamic and interactive model that reflects the actual physical structure [22]. This allows engineers to continuously monitor conditions and optimize the stability of infrastructure projects in real time [23]. When combined with BIM, it provides a detailed digital overview of a facility's physical and functional elements; the integration forms a comprehensive framework [24; 25]. This approach enhances the ability to simulate, monitor, and optimize the construction projects throughout their entire lifecycle [26]. The use of DT in underground engineering, especially for large caverns, is still in its early stages [27]. A major challenge in this field is effectively integrating real-time data from various sources, including geological surveys, laboratory experiments, and construction site sensors, into a unified virtual model [28]. This model must be capable of accurately predicting and responding to changes in rock mass behavior [29]. This challenge becomes even more complex due to the necessity for interoperability among various underground construction activities and data formats [30]. Ensuring smooth data exchange and effective collaboration among project stakeholders is essential to addressing this issue. Recent developments in information technology and data analytics have made it more practical to adopt digital twin-based frameworks in underground engineering [31; 32]. However, traditionally, underground construction projects rely heavily on hardcopy records, paper-based reports, and flat-file storage systems (e.g., spreadsheets, PDFs, and basic CAD drawings) [33].