PSI - Issue 84

Enrico Pasquale Zitiello et al. / Procedia Structural Integrity 84 (2026) 360–367

367

The application of the methodology to the two viaducts demonstrated the effectiveness of integrating BIM models, IoT sensors and data analysis platforms in structural monitoring. The Digital Twin was configured as a dynamic system capable of continuously connecting the real infrastructure to the virtual model, allowing constant updates on the progress of the project based on data acquired in real time. In the case studies analysed, the closed-loop workflow made it possible to consistently structure all stages of the process, from initial modelling to verification of the effectiveness of the interventions. On the first viaduct, the system made it possible to monitor the evolution of the cracking pattern and the rotations of the monitored shoulder; on the second, the distributed approach allowed for an integrated assessment of the static and dynamic behaviour of the entire structure. In both cases, the Digital Twin supported structural diagnosis and maintenance planning, facilitating the transition from reactive to predictive management based on objective data. The integration between the digital model and real data also enabled the development of a historical information base, essential for analysing development trends and defining alert and intervention thresholds.The ability to compare pre- and post-construction conditions completes the methodological cycle and progressively improves the reliability of the system. Key future developments include the integration of artificial intelligence algorithms for automatic anomaly identification and structural behaviour prediction, as well as the extension of the sensor network with advanced technologies and territorial-scale monitoring systems. Further developments may include the automatic calibration of numerical models through model updating procedures and integration with risk management and resilience assessment tools. Overall, the results obtained confirm the validity and scalability of the proposed approach, highlighting how the Digital Twin represents a concrete solution for safer, more efficient and sustainable management of existing infrastructure. References [1]Wimmer J, Braml T. Digital twins for engineering structures—An Industry 4.0 perspective. Structural Concrete. 2024; 25(6): 4202–4218. https://doi.org/10.1002/suco.202400683 [2] Zhao Z., Gao Y., Hu X., Zhou Y., Zhao L., Qin G., Guo J., Liu Y., Yu C., Han D. (2019). Integrating BIM and IoT for smart bridge management. IOP Conf. Series: Earth and Environmental Science 371 (2019) 022034. doi:10.1088/1755-1315/371/2/02203. [3] Villa, Valentina & Naticchia, Berardo & Bruno, Giulia & Aliev, Khurshid & Piantanida, Paolo & Antonelli, Dario. (2021). IoT Open-Source Architecture for the Maintenance of Building Facilities. Applied Sciences. 11. 5374. 10.3390/app11125374. [4] Mousavi V., Rashidi M., Mohammadi M., Samali B. (2024). Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions. Remote Sens. 2024, 16(11), 1887. https://doi.org/10.3390/rs16111887 [5]Deng M., Menassa C.C., Kamat V.R. (2021). From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry. Journal of Information Technology in Construction - ISSN 1874-4753. DOI: 10.36680/j.itcon.2021.005. [6] Abdelalim, Ahmed & Shawky, Kamal & Salem, Mohamed & Alnaser, Aljawharah & Sherif, Alaa. (2025). Digital Transformation of BIM Execution Plans for Effective BIM Implementation in Mega Construction Projects Research Article Corresponding Author Citation. 2. 1-15. [7] Zitiello, Enrico & Porcellini, Francesca & Salzano, Antonio & Nicolella, Maurizio. (2025). Digital Revolution for Building Management: A Methodological Proposal Using Advanced Technologies. 10.1007/978-3-032-06978-8_47. [8] Eneyew D. D., Capretz M. A. M., Bitsumlak G. T. (2022). Toward Smart-Building Digital Twins: BIM and IoT Data Intefration. IEEE Access, vol. 10, pp. 130487-130506, 2022. doi: 10.1109/ACCESS.2022.3229370. [9] Ghosh A., Edwards D. J., Hosseini M.R. (2021). Patterns and trends in Internet of Things (IoT) research: future applications in the construction industry. Engineering, Construction and Architectural Management, Vol. 28 No. 2, pp. 457-481. https://doi.org/10.1108/ECAM-04-2020-0271. [10] Tang S., Shelden D. R., Eastman C. M., Pishdad-Bozorgi P., Gao X. (2019). A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Automation in Construction, Volume 101, May 2019, Pages 127-139. https://doi.org/10.1016/j.autcon.2019.01.020. [11] Scianna A., Gaglio G. F., La Guardia M. (2022). Structure Monitoring with BIM and IoT: The Case Study of a Bridge Beam Model. ISPRS Int. J. Geo-Inf. 2022, 11, 173. https://doi.org/10.3390/ijgi11030173. [12] Zinno R., Haghshenas S. S., Guido G., Vitale A. (2022). Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art. IEEE Access, vol. 10, pp. 88058-88078, 2022. doi: 10.1109/ACCESS.2022.3199443. [13] Al-Ali A.R., Beheiry S., Alnabulsi A., Obaid S., Mansoor N., Odeh N., Mostafa A. (2024) An IoT-Based Road Bridge Health Monitoring and Warning System. Sensors 2024, 24, 469. https://doi.org/10.3390/s24020469. [14] Chen C., Fu H., Zheng Y., Tao F., Liu Y. (2023). The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, Volume 71, 2023, Pages 581-594, ISSN 0278-6125. https://doi.org/10.1016/j.jmsy.2023.10.010.