Structural Health Monitoring & Machine Learning, Vol. 12

148 M. Berardengo et al. mode shapes are processed using the PCA, excluding the first principal components (first eleven) affected by environmental factors by exploiting the CVCR method. The remaining principal scores, which are immune to ambient influences, are analyzed using statistical tools such as the MSD. Outliers in the proposed DI are interpreted as changes in the dynamic behavior of the structure caused by potential damage incoming. In order to validate the strategy proposed, two different damages have been induced to the structure: the positioning of different masses and the reduction of the tightening torque of the bolts in the connection elements between rods. Results obtained from the DI proposed demonstrate that the synthetic index effectively detects structural damage while remaining highly resistant to ambient variations. References 1. C. R. Farrar and K. Worden, Structural Health Monitoring. Wiley, 2012. doi:10.1002/9781118443118. 2. Wei Fan and Pizhong Qiao, “Vibration-based Damage Identification Methods: A Review and Comparative Study,” Struct Health Monit, vol. 10, no. 1, pp. 83–111, Jan. 2011, doi:10.1177/1475921710365419. 3. M. Radzien´ski, M. Krawczuk, and M. Palacz, “Improvement of damage detection methods based on experimental modal parameters,” Mech Syst Signal Process, vol. 25, no. 6, pp. 2169–2190, Aug. 2011, doi:10.1016/j.ymssp.2011.01.007. 4. M. S. Cao, G. G. Sha, Y. F. Gao, and W. Ostachowicz, “Structural damage identification using damping: a compendium of uses and features,” Smart Mater Struct, vol. 26, no. 4, p. 043001, Apr. 2017, doi:10.1088/1361-665X/aa550a. 5. A. K. Pandey, M. Biswas, and M. M. Samman, “Damage detection from changes in curvature mode shapes,” JSoundVib, vol. 145, no. 2, pp. 321–332, Mar. 1991, doi:10.1016/0022-460X(91)90595-B. 6. C. R. Farrar, S. W. Doebling, and D. A. Nix, “Vibration–based structural damage identification,” Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 359, no. 1778, pp. 131–149, Jan. 2001, doi:10.1098/rs ta.2000.0717. 7. S. W. , and C. R. F. Doebling, “Using statistical analysis to enhance modal-based damage identification,” DAMAS. Vol. 97., 1997. 8. N. P. Raut, A. B. Kolekar, and S. L. Gombi, “Optimization techniques for damage detection of composite structure: A review,” Mater Today Proc, vol. 45, pp. 4830–4834, 2021, doi:10.1016/j.matpr.2021.01.295. 9. S. Gres´, M. Do¨hler, and L. Mevel, “Uncertainty quantification of the Modal Assurance Criterion in operational modal analysis,” Mech Syst Signal Process, vol. 152, p. 107457, May 2021, doi:10.1016/j.ymssp.2020.107457. 10. A. Deraemaeker and K. Worden, “On the use of the Mahalanobis squared-distance to filter out environmental effects in structural health monitoring,” MATEC Web of Conferences, vol. 16, p. 02004, Sep. 2014, doi:10.1051/matecconf/20141602004. 11. B. Peeters, H. Van der Auweraer, P. Guillaume, and J. Leuridan, “The PolyMAX Frequency-Domain Method: A New Standard for Modal Parameter Estimation?,” Shock and Vibration, vol. 11, no. 3–4, pp. 395–409, 2004, doi:10.1155/2004/523692. 12. K. Pearson, “LIII. On lines and planes of closest fit to systems of points in space.,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 2, no. 11, pp. 559–572, Nov. 1901, doi:10.1080/14786440109462720.

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