PSI - Issue 84

Roberto Acerbis et al. / Procedia Structural Integrity 84 (2026) 765–772

771

4. Conclusions This paper presented a structured workflow for the design, implementation, and operational management of structural monitoring systems applied to bridges and viaducts of the ASPI motorway network within the PNRR framework. The proposed approach integrates technical, technological, and organizational aspects into a unified process aimed at transforming monitoring data into effective decision-support tools for infrastructure management. A key element of the workflow is the distinction between Data-Driven and Model-Based monitoring strategies, enabling adaptation of objectives, instrumentation layouts, and analysis methodologies according to the available level of structural knowledge. Standardized procedures for data validation, pre-processing, and interpretation represent a fundamental prerequisite to ensure the reliability and engineering significance of information derived from long-term monitoring. Particular emphasis was placed on operational management of monitoring systems through threshold-based procedures governing the transition from ordinary operation to extraordinary management phases. The adoption of multi-level thresholds and codified actions enables progressive and proportionate risk management, promoting timely interventions while reducing the likelihood of unnecessary actions. Application of the proposed workflow to already instrumented structures within the PNRR project confirmed its operational feasibility and scalability across an extended infrastructure network. The experience gained highlights how a well-defined and shared monitoring management process is essential to enhance structural awareness, optimize maintenance planning, and support prioritization of interventions based on objective data. Overall, the described workflow represents a replicable and adaptable model for other infrastructure contexts, contributing to the evolution of structural monitoring from a simple data acquisition tool to an integral component of modern asset management strategies. Ju, H., Deng, Y., Zhai, W., & Li, A. (2022). Recovery of abnormal data for bridge structural health monitoring based on deep learning and temporal correlation. Sensors and Materials, 34(12), 4491–4505. Kromanis, R., & Kripakaran, P. (2021). Performance of signal-processing techniques for anomaly detection using a temperature-based measurement interpretation approach. Journal of CivilStructural Health Monitoring, 11(3), 607–626 Glashier, M., Kromanis, R., & Buchanan, R. (2024). Iterative regression-based thermal response prediction for bridge structural health monitoring. Advanced Engineering Informatics, 59, 102309 Kromanis, R., & Kripakaran, P. (2016). Predicting thermal response of bridges using regression models derived from measurement histories. Journal of Civil Structural Health Monitoring, 6(2), 247–254. Kromanis, R., & Kripakaran, P. (2017). Data-driven approaches for measurement interpretation and anomaly detection in structural health monitoring. Advanced Engineering Informatics, 33, 190–204. Gikas, V., Papadopoulos, K., & Kotsakis, C. (2019). Inclinometer-based bridge monitoring under environmental loading. In Proceedings of the 4th Joint International Symposium on Deformation Monitoring (JISDM 2019). Buckley, C., Kromanis, R., & Cross, E. J. (2021). Dynamic harmonic regression for separating environmental and operational effects in structural health monitoring data. Structural Health Monitoring, 20(6), 2803–2822. Maryland State Highway Administration. (2008). Investigation of a wireless remote bridge monitoring system (Research Report No. MD-08 SP109B4D). Maryland DOT Research Division Resensys. (n.d.). Bridge bearing & expansion joint monitoring – Practical deployment guidance: tiltgauge response vs. temperature obtained via regression analysis to set alerts for stuck/frozen bearings (Application Note). Resensys – Technical Documentation Borlenghi, G., et al. (2022). Two-year tiltmeter rotations vs. temperature on a masonry arch bridge: nearly linear correlations; later a permanent shift in the rotation–temperature regression flags an anomaly (Authorproof chapter). Politecnico di Milano – Department of Civil and Environmental Engineering Khandani, A. (2016). Field deployment on a highway bridge: linear regression versus temperature for each wireless inclinometer sensor to determine inclination dependence on temperature (DOT Research Report). Maryland State Highway Administration / University of Maryland. Farrar, C. R., & Worden, K. (2012). Structural Health Monitoring: A Machine Learning Perspective. Wiley. Collins, J., Mullins, G., Lewis, C., & Winters, D. (2014). State of the Practice and Art for Structural Health Monitoring of Bridge Substructures (FHWA-HRT-09-040). Federal Highway Administration. Künsch, H. R. (1989). The jackknife and the bootstrap for general stationary observations. Annals of Statistics, 17(3), 1217–1241. Lahiri, S. N. (2003). Resampling Methods for Dependent Data. Springer. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. References

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