PSI - Issue 84

Vincenzo Gattulli et al. / Procedia Structural Integrity 84 (2026) 41–48

48

responses should be interpreted not only as an initial assessment but also as a basis for future model updating procedures. Such procedures can progressively improve the predictive capability of the Digital Twin by reducing the gap between observed and simulated structural behavior. 5.4. Real-Time Performance and Latency Considerations The adopted data acquisition and transmission chain enables near real-time availability of monitoring data for processing and visualization within the Digital Twin environment. Under typical operating conditions, the end-to-end latency between data acquisition at the sensing node and data availability at the server is on the order of a few seconds. This performance enables timely access and analysis of vibration and environmental measurements, supporting continuous monitoring of the underground infrastructure. The integration of low-latency data transmission with dynamic characterization techniques facilitates efficient interpretation of variations in structural response and supports This paper presents a real-time Digital Twin framework for an operational underground research hall at the Laboratori Nazionali del Gran Sasso (LNGS), integrating high-fidelity geometric modelling, multisensor monitoring, and near-real-time data processing under strict environmental and operational constraints. The Hall B case study demonstrates the feasibility of combining three-dimensional geometry with continuous structural and environmental monitoring. Frequency-domain analysis and stochastic subspace identification enabled the identification of modal properties from experimental data acquired within the COSINUS setup. A comparison with numerical results obtained from a finite element model of the LUNA experimental area provided a preliminary assessment of the relationship between experimental observations and model-based predictions. The observed differences highlight the influence of structural configuration, boundary conditions and modelling assumptions. In this context, the proposed Digital Twin should be interpreted primarily as an integrated real-time platform for combining geometric reconstruction, monitoring data, and model-based structural interpretation, rather than as a fully validated predictive tool. The present results establish the technical and methodological basis for future developments toward model updating, automated anomaly detection, and predictive maintenance, which will require long-term monitoring data and closer correspondence between experimental and numerical configurations. In particular, integrating automated operational modal analysis with machine learning techniques will enable adaptive model updating and the interpretation of environmental effects on structural response (Crognale et al., 2026). References Crognale, M., De Iuliis, M., Rinaldi, C., & Gattulli, V. (2023). Damage detection with image processing: a comparative study. Earthquake Engineering and Engineering Vibration, 22(2), 333-345. ITA Working Group on Structural Health Monitoring, 2022. Guidelines for structural health monitoring of tunnels. Tunnelling and Underground Space Technology. Farrar, C.R., Worden, K., 2007. An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A 365(1851), 303– 315. Grieves, M., Vickers, J., 2017. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Rinaldi, C., Di Sabatino, U., Potenza, F., Gattulli, V., 2021. Robotized inspection and health monitoring in the Gran Sasso National Laboratory. Structural Monitoring and Maintenance 8(1), 51–67. Brownjohn, J.M.W., 2007. Structural health monitoring of civil infrastructure. Philosophical Transactions of the Royal Society A 365(1851), 589– 622. Talebi, A., Crognale, M., & Gattulli, V. (2026). Point-cloud-driven structural assessment of masonry vaults with coupled laser intensity and FE stress mapping. Engineering Structures, 352, 122112. Menna, F., Spera, M., Remondino, F., 2014. State of the art in high-density image matching. The Photogrammetric Record 29(146), 144–166. Bayer, B., et al., 2018. Georeferencing techniques for underground and GNSS-denied environments. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Crognale, M., Rinaldi, C., Ciambella, J., & Gattulli, V. (2025). Machine learning–enhanced structural health monitoring of the steel–glass exedra under year-round environmental loading. Journal of Low Frequency Noise, Vibration and Active Control , 14613484261421404. data-informed monitoring strategies. 6. Conclusions and Future Works