PSI - Issue 62

E. Tomassini et al. / Procedia Structural Integrity 62 (2024) 903–910 Author name / Structural Integrity Procedia 00 (2019) 000–000

910

8

Table 2. SSI-cov identification parameters and stability thresholds. SSI-cov parameters

Stability threshold ��� ��� � � � � 80 120 0.02 0.05 0.01 -

Subgroup 1 Subgroup 2 Subgroup 3 Subgroup 4

3.5 s

3 s 2 s 2 s

190 200

230 240 120

- - -

- - -

- - -

0.03 0.04

90 0.04 modes, respectively, and channels 32 and 48 are positioned in transversal and vertical directions as well. Therefore, the MLR model of each subgroup was defined by considering all the identified frequencies as estimators, and the predictors were the temperature acquisitions and the RMS values of the accelerations of all the involved channels. Residuals between the statistical models and tracked frequencies were computed to establish the Hotelling’s T2 statistical distance and define control charts. Control charts for all subgroups during the training period are depicted in Fig. 4 (e), (f), (g), (h). Two Upper Control Limits (UCL) were set in the control charts, corresponding to confidence levels of 95% and 99% in the statistical distribution of residuals. 4. Conclusions The current work presented a methodology to carry out continuous SHM of densely instrumented bridges able to reduce the comprehensive computational burden of the analysis when computational power is a limiting factor. The bottleneck of the whole process is identified as the automated modal identification of the continuously recorded time histories of acceleration and, particularly, the SVD of the Toeplitz matrix in the SSI-cov identification algorithm. Therefore, the strategy proposed in this work is to reduce the computational memory and time needed for the computation of the SVD by discretizing the modal identifications in different subgroups. An illustrative application of the methodology to a case study has been also presented demonstrating the ongoing monitoring of the structure by mean of different control charts. Acknowledgements This study was supported by FABRE – “Research consortium for the evaluation and monitoring of bridges, viaducts and other structures” (www.consorziofabre.it/en) within the activities of the FABRE-ANAS 2021-2024 research program. The work was also supported by the Italian Ministry of Education, University and Research (MIUR) through the funded project of national interest “TIMING – Time evolution laws for IMproving the structural reliability evaluation of existING post-tensioned concrete deck bridges” (Protocol No. P20223Y947). Any opinion expressed in the paper does not necessarily reflect the view of the funders. References García-Macías, E., Ruccolo, A., Zanini, M. A., Pellegrino, C., Gentile, C., Ubertini, F., Mannella, P., 2022. P3P: a software suite for autonomous SHM of bridge networks, Journal of Civil Structural Health Monitoring, 1-18. García-Macías, E., Ubertini, F., 2020. MOVA/MOSS: Two integrated software solutions for comprehensive Structural Health Monitoring of structures, Mechanical Systems and Signal Processing, 143:106830. Kullaa, J., 2003. Damage detection of the Z24 bridge using control charts, Mechanical Systems and Signal Processing 17, 163–170. Magalhães, 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. Magalhães, F., Cunha, A., Caetano, E.,2009. Online automatic identification of the modal parameters of a long span arch bridge. Mechanical Systems and Signal Processing. 23. 316-329. Montgomery, D. C., 1997. Introduction to statistical quality control (6th Ed.), John Wiley & Sons, New York, pp 728. Peeters, B., De Roeck, G., 2001. One–year monitoring of the Z24–Bridge: environmental effects versus damage events. Earthquake Engineering and Structural Dynamics, 30 149–171. Reynders, E., Houbrechts, J., De Roeck, G. (2012). Fully automated (operational) modal analysis. Mechanical Systems and Signal Processing, 29: 228-250. Tomassini E., García-Macías E., Reynders E., Ubertini F., 2023. Model-assisted clustering for automated operational modal analysis of partially continuous multi-span bridges, Mechanical Systems and Signal Processing, 200:110587. Yan, A.M., Kerschen, G., De Boe, P., Golinval. J.C., 2005. Structural damage diagnosis under varying environmental conditions–part I: a linear analysis, Mechanical Systems and Signal Processing 19, 847–864. Yan, A.M., Kerschen, G., De Boe, P., Golinval. J.C., 2005. Structural damage diagnosis under varying environmental conditions–part II: local PCA for non-linear cases, Mechanical Systems and Signal Processing 19, 865–880.