PSI - Issue 78

Marco Martino Rosso et al. / Procedia Structural Integrity 78 (2026) 301–308

308

Responses were recorded by six sensors per specimen, installed on the reinforced frames of the buildings. Acceleration responses collected by the sensors were processed using Frequency Domain Decomposition (FDD) and covariance-based subspace-based identification techniques, i.e., output-only operational modal analysis (OMA) methods implemented in PyOMA2 software. To optimize raw data quality under noisy conditions with alternating sampling intervals, interpolation resampling was applied to map all records onto a uniform 1 kHz time base. The preliminary results presented herein were validated with a finite element model of the RC building under investigation implemented in the Scientific Toolkit for OpenSees (STKO) software. These preliminary findings validate the effects of strengthening of the retrofitting system on the case study RC buildings, and even evidencing robustness of noise-based modal identification for evaluation of progressive damage accumulation due to seismic sequence events, such as in aftershock scenarios. Future studies may also explore input-output dynamic characterization techniques, or even machine learning-based damage detection based on neural transformers for time series forecasting and capturing damage as prediction reconstruction errors, searching for innovative techniques that can even guide future large-scale shake table tests paradigms in the earthquake engineering field for risk mitigation and structural health monitoring purposes. Acknowledgements Marco Martino Rosso, Giuseppe Carlo Marano, Guido Camata, and Giuseppe Quaranta acknowledge the support received through the project `Artificial Intelligence for SUstainable seismic risk reduction of STructures (AI SUST)'' (project code: 2022LEFKHS) funded by European Union -- NextGeneration EU through the PRIN 2022 program of the Italian Ministry of University and Research (MUR) (D. D. n. 104, 02-02-2022). This work reflects only the authors' views and opinions whereas the MUR cannot be considered responsible for them. References Rainieri, C. (2008). Operational Modal Analysis for seismic protection of structures. University of Naples “FEDERICO II . Gara, F., Carbonari, S., Roia, D., Balducci, A., & Dezi, L. (2021). Seismic retrofit assessment of a school building through operational modal analysis and fe modeling. Journal of Structural Engineering , 147 (1), 04020302. Combey, A., & Mercerat, E. D. (2025). Continuous Seismic Monitoring of a Colonial Church in Cusco, Peru: Unveiling Remarkable Soil Structure Interaction and Nonlinear Dynamics. International Journal of Architectural Heritage , 1-19. Rainieri, C., Fabbrocino, G., Manfredi, G., & Dolce, M. (2012). Robust output-only modal identification and monitoring of buildings in the presence of dynamic interactions for rapid post-earthquake emergency management. Engineering Structures, 34, 436-446. Rosso, M. M., Aloisio, A., Parol, J., Marano, G. C., & Quaranta, G. (2023). Intelligent automatic operational modal analysis. Mechanical Systems and Signal Processing , 201 , 110669. Zhou, K., & Li, Q. S. (2022). Modal identification of high-rise buildings under earthquake excitations via an improved subspace methodology. Journal of Building Engineering , 52 , 104373. Liao, C. M. (2025). Modal and Wave Propagation Analysis of Vibration Tests on a Laboratory Building Model Before and After Damage. Structural Control and Health Monitoring, 2025(1), 3453150. Rebecchi, G., Calvi, P. M., Bussini, A., Dacarro, F., Bolognini, D., Grottoli, L., Rosti, M., Ripamonti, F., & Cii, S. (2023). Full-scale shake table tests of a reinforced concrete building equipped with a novel servo-hydraulic active mass damper. Journal of Earthquake Engineering, 27(10), 2702-2725. Astroza, R., Ebrahimian, H., Conte, J. P., Restrepo, J. I., & Hutchinson, T. C. (2022). Statistical analysis of the modal properties of a seismically damaged five-story RC building identified using ambient vibration data. Journal of Building Engineering, 52, 104411. Aceto, L., Amelio, A., Boccagna, R., Bottini, M., Camata, G., Germano, N., & Petracca, M. (2022, August). A self-consistent artificial intelligence-based strategy for structural health monitoring. In Fifth International Conference on Railway Technology: Research, Development and Maintenance (RAILWAYS 2022). Vaccari, F., Suhadolc, P., & Panza, G. F. (1990). Irpinia, Italy, 1980 earthquake: waveform modelling of strong motion data. Geophysical Journal International , 101 (3), 631-647. PalChaudhuri, S., Saha, A. K., & Johnson, D. B. (2004, April). Adaptive clock synchronization in sensor networks. In Proceedings of the 3rd international symposium on Information processing in sensor networks (pp. 340-348). Pasca, D. P., Margoni, D. F., Rosso, M. M., & Aloisio, A. (2024, May). PyOMA2: An Open-Source Python Software for Operational Modal Analysis. In International Operational Modal Analysis Conference (pp. 423-434). Cham: Springer Nature Switzerland.