PSI - Issue 78

Antonio Sánchez López-Cuervo et al. / Procedia Structural Integrity 78 (2026) 1791–1798

1798

4. Conclusions

This paper presents the main results of the dynamic monitoring of a laboratory-scale steel frame using a multi sensor system composed of accelerometers and strain gauges. Firstly, the e ff ect of damage on the modal parameters identified by both sensing systems was analysed. The results highlight the value of combining data from di ff erent sensor types to reduce the uncertainty associated with the sensors themselves and the modal identification techniques. Secondly, the modal data obtained in the undamaged scenario were used to update a finite element model. A multi objective optimization approach was formulated to jointly consider the measurements from both sensor systems, resulting in a set of optimal solutions defined by the Pareto front. The calibrated model shows strong agreement with the experimental modal data, validating the proposed methodology. Future work will focus on using the modal parameters identified in the various damage scenarios to localize the a ff ected joints, starting from the calibrated finite element model.

Acknowledgements

Antonio S. Lo´pez-Cuervo is supported by a FPU contract-fellowship from the Spanish Ministry of Universities Ref: FPU22 / 03667. The authors would like to acknowledge the financial support provided by the Spanish Ministry of Science and Inno vation under the research project SMART-BRIDGES [PLEC2021-007798] funded by the FEDER NextGenerationEU recovery plan.

References

Anastasopoulos, D., De Roeck, G., Reynders, E. P. B., 2019. Influence of damage versus temperature on modal strains and neutral axis positions of beam-like structures. Mechanical Systems and Signal Processing 134, 106311. Anastasopoulos, D., Reynders, E., De Roeck, G., 2020. Structural Health Monitoring Based on Operational Modal Analysis from Long Gauge Dynamic Strain Measurements. Doctoral Thesis. Anastasopoulos, D., Reynders, E. P. B., 2025. Dynamic strain-based monitoring of a historical Vierendeel truss bridge under changing environmen tal and support conditions. Journal of Civil Structural Health Monitoring 15(5), 1465–1492. Blank, J., Deb, K., 2020. PYMOO: Multi-Objective Optimization in Python. IEEE Access 8, 89497–89509. C¸ elebi, M., 2019. S2HM of Buildings in USA. In: Limongelli, M. P., C¸ elebi, M. (Eds.), Seismic Structural Health Monitoring: From Theory to Successful Applications. Springer Tracts in Civil Engineering. Springer, Cham, pp. 3–30. Garc´ıa–Mac´ıas, E., Ubertini, F., 2020. MOVA / MOSS: Two integrated software solutions for comprehensive Structural Health Monitoring of struc tures. Mechanical Systems and Signal Processing 143, 106830. Kralovec, C., Schagerl, M., 2020. Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures. Sensors 20, 826. Lieven, N. A. J., Ewins, D. J., 1988. Spatial correlation of mode shapes, the coordinate modal assurance criterion (COMAC). Proceedings of the 6th International Modal Analysis Conference, vol. 1, Kissimmee, FL, USA, pp. 690–695. Limongelli, M. P., 2019. Damage localization through vibration based S2HM: a survey. In: Limongelli, M. P., C¸ elebi, M. (Eds.), Seismic Structural Health Monitoring: From Theory to Successful Applications. Springer Tracts in Civil Engineering. Springer, Cham, pp. 217–236. Magalha˜es, F., Caetano, E., Cunha, A., 2008. Operational modal analysis and finite element model correlation of the Braga Stadium suspended roof. Engineering Structures 30(6), 1688–1698. Magalha˜es, F., Cunha, A., Caetano, E., 2012. Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection. Mechanical Systems and Signal Processing 28, 212–228. Reynders, E., De Roeck, G., Bakir, P., Sauvage, C., 2007. Damage identification on the Til ff Bridge by vibration monitoring using optical fiber strain sensors. Journal of Engineering Mechanics 133(2), 185–193.

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