## 1 Introduction  
In recent decades, rapid urbanization and the expansion of metropolitan transportation networks have led to a growing reliance on underground infrastructures, particularly urban tunnels [1, 2, 3]. Though urban tunnels play a pivotal role in ensuring the smooth flow of traffic, their enclosed and complex nature makes them particularly vulnerable to emergencies such as fires, floods, and traffic accidents [4]. Moreover, the restricted access and limited evacuation routes within these tunnels often hinder timely rescue effortsand exacerbate the impact of such incidents. Hence, an effective emergency response in such environments is crucial to minimizing casualties, reducing economic losses, and maintaining public safety [5,6]. In urban underground tunnel construction, the disturbance to surrounding buildings and infrastructure has become a critical concern [7,8]. Numerous studies have focused on understanding the mechanisms of such disturbances, particularly the impact of construction activities on building settlement and ground deformation. For instance, research has extensively examined how tunneling-induced soil instability and abnormal settlement can lead to structural damage or even catastrophic failures [9, 10, 11]. When such risks materialize--whether in the form of sudden ground subsidence causing road collapse or disruptions to shield tunneling, such as segment misalignment--effective emergency management becomes paramount. Traditional approaches to addressing these risks primarily rely on real-time monitoring systems to detect anomalies, followed by the issuance of alerts and the activation of emergency response protocols based on established standards and guidelines [12]. While these methods have been widely adopted, they are not without significant shortcomings. First, the efficiency of traditional monitoring and response systems is often limited by delays in data collection, analysis, and decision-making, which can exacerbate the severity of an incident [13]. Second, the coordination between different stakeholders, such as construction teams, emergency responders, and local authorities, tends to be slow and fragmented, leading to inadequate or delayed interventions [14]. Third, traditional methods often lack the capability to predict or preemptively address potential risks, as they are primarily reactive rather than proactive [15]. Additionally, the reliance on standardized protocols may not account for the unique complexities of each construction site, such as varying soil conditions, building densities, or traffic loads, resulting in suboptimal responses. With the rapid advancement of technology, an increasing number of studies have begun to explore the application of digital solutions to enhance emergency management in urban underground tunnel construction [16, 17, 18, 19]. For example, [PERSON] et al. [17] proposed a multi-utility tunnel data model based on building information modeling (BIM) for emergency response in tunnels. Several studies have also used digital solutions in tunnel construction using tunnel boring machines [20, 21, 22, 23, 24, 25]. Among these technologies, digital twin technology has emerged as a transformative tool, offering a comprehensive and dynamic approach to addressing the challenges associated with construction-induced risks [26, 27, 28]. A digital twin creates a virtual replica of the physical tunnel and its surrounding environment, enabling real-time monitoring, simulation, and predictive analysis [29]. This technology allows for the early detection of anomalies, such as soil instability or abnormal settlement, and provides actionable insights to mitigate risks before they escalate. By integrating data from sensors, geological surveys, and construction processes, digital twins facilitate more informed decision-making, improved resource allocation, and enhanced coordination among stakeholders [30]. As mentioned earlier, effective emergency management in this context also relies heavily on established standards, regulations, and expert knowledge [14].