The strategic placement of sensors is critical for ensuring the effectiveness and reliability of valuable Digital Twin systems, especially in complex underground infrastructures. In Galleria B of the Laboratori Nazionali del Gran Sasso (LNGS), where dynamic experimental activities and periodic reconfigurations are common, an optimal sensor deployment strategy will be developed to maximize structural and energetic information capture while minimizing redundancy and maintenance complexity. The proposed methodology will combine expert-driven prioritization with data-driven variability and correlation analysis to define the optimal number and positioning of sensors across multiple loading and environmental conditions. The sensor network will encompass both structural and environmental monitoring objectives, ensuring that the developed methodology addresses comprehensive data acquisition needs for future Digital Twin applications. Sensor metadata and spatial configurations will be managed within a Building Information Modeling (BIM) platform to enable real-time updates, integration with experimental infrastructure, and system scalability over time. Given the evolving nature of experimental setups within LNGS Galleria B, particular emphasis will be placed on establishing a flexible and adaptive sensor network capable of supporting continuous structural health assessment and energy efficiency under changing operational scenarios. The envisioned methodology is expected to form the foundation for a dynamic Digital Twin of Galleria B, facilitating predictive maintenance, adaptive reconfiguration, and long-term resilience evaluation. This future work aims to establish an integrated and scalable framework for optimizing sensor deployment and digital asset management in one of the world’s largest and most complex underground research laboratories.