DEVELOPMENT OF A HYBRID METHOD TO DETECT STRUCTURAL DAMAGE
DOI:
https://doi.org/10.26512/ripe.v2i15.21387Keywords:
Structural Dynamics. Damage Identification. Computational Intelligence.Abstract
Structural damage detection using dynamic measurements has led to the development of several techniques in the last decades. Most of these methods associate modal variations of the structure to damage like methods based on strain energy deviation, methods based on changes in curvature mode shapes, flexibility matrix analysis, etc. Although these techniques aforementioned are mostly efficient to identify structural alterations in numerical models, they have difficulties in practical applications with experimental data. Thus, hybrid methods to detect the presence of damage directly from raw dynamic measurements in addition to structural modal characteristics can be a promising field of research, involving strategies based on artificial intelligence and higher-order statistics. This work aims to present the preliminary results of a hybrid method to detect structural damage. Using modal data and also higher-order statistics of structural time histories as inputs of artificial intelligence algorithms, the viability of the proposed methodology is initially evaluated. Two applications are analyzed: a simply supported numerical beam and an experimental tested prototype concrete slab. The good results achieved motivate the continuous development of the proposed hybrid method.
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Alvandi, A., Cremona, C., 2002. “Reliability of bridge integrity assessment by dynamical testing”. First European Workshop on Structural Health Monitoring, Ecole Normale Supérieure de Cachan.
Alvandi, A., Cremona, C., 2006. "Assessment of vibration-based damage identification techniques". Journal of Sound and Vibration, vol. 292, n. 1, pp. 179-202.
Alves, V. N., 2012. “Estudo de novas estratégias para identificação de danos estruturais partir de dados vibracionais”. Master’s dissertation, Universidade Federal de Ouro Preto.
Alves, V., Cury, A., Roitman, N., Magluta, C., Cremona, C., 2015. “Structural modification assessment using supervised learning methods applied to vibration data”. Engineering Structures, vol. 99, pp.439-448.
Allemang, R., Brown, D., 1982. “A correlation coefficient for modal vector analysis”. Proceedings of the 1st International Modal Analysis Conference, SEM, Orlando, pp. 110-116.
Barbosa, F. S., Cremona C., 2001. "Identification Modale de Structures Sous Sollicitation Ambiante." Final report, Laboratoire Central des Ponts et Chausses (LCPC), Vols 1.
Bishop, C. M., 2006. “Pattern recognition”. Machine Learning, vol. 128.
Cachot, E., Vayssade, T., Virlogeux, M., Lancon, H., Hajar, Z., Servant, C., 2015. “The Millau Viaduct: Ten Years of Structural Monitoring”. Structural Engineering International, vol. 25, n. 4, pp. 375-380.
Cawley, P., Adams, R., 1979. “The location of defects in structures from measurements of natural frequencies”. Journal of Strain Analysis for Engineering Design, vol. 14, n. 2, pp. 49-57.
Cury, A. A., Borges, C. C., Barbosa, F. S., 2011. “A two-step technique for damage assessment using numerical and experimental vibration data”. Structural Health Monitoring, vol. 10, n. 4, pp. 417-428.
De la Rosa, J. J. G., Agüera-Pérez, A., Palomares-Salas, J. C., Moreno-Muñoz, A., 2013. “Higher-order statistics: Discussion and interpretation”. Measurement, vol. 46, n. 8, pp. 2816-2827.
Fox, C. H. J., 1992. “The location of defects in structures: A comparison of the use of natural frequency and mode shape data”. Proceedings of the International Modal Analysis Conference, SEM Society for Experimental Mechanics Inc., pp. 522-528.
Hagan, M. T., Menhaj, M. B., 1994. “Training feedforward networks with the Marquardt algorithm”. Neural Networks, IEEE Transactions on, Vol. 5, No. 6, pp. 989-993.
Haritos, N., Owen, J. S., 2004. “The use of vibration data for damage detection in bridges: a comparison of system identification and pattern recognition approaches”. Structural Health Monitoring, vol. 3, n. 2, pp. 141-163.
Iwasaki, A., Todoroki, A., Shimamura, Y., Kobayashi, H., 2004. “An unsupervised statistical damage detection method for structural health monitoring (applied to detection of delamination of a composite beam)”. Smart Materials and Structures, vol. 13, n. 5, pp. N80.
Kohavi, R., 1995. "A study of cross-validation and bootstrap for accuracy estimation and model selection." Ijcai, vol. 14, n. 2, pp. 1137-1145.
Kawiecki, G., 2001. “Modal damping measurement for damage detection”. Smart Materials and Structures, vol. 10, n. 3, p. 466-471.
Kim, J., Stubbs, N., 1993. “Assessment of the relative impact of model uncertainties on the accuracy of global nondestructive damage detection in structures”. Technical report, New Mexico University, United States.
Lieven, N., Ewins, D., 1988. “Spatial correlation of mode shapes”. Proceeding of the sixth International Modal Analysis Conference, Kissimmee, pp. 690-695.
Messina, A., Williams, E., Contursi, T., 1998. “Structural damage detection by a sensitivity and statistical-based method”. Journal of Sound and Vibration, vol. 216, n. 5, pp. 791-808.
Ndambi, J.; Vantomme, J., De Visscher, J., 2000. “Modal damping as a damage detection parameter in reinforced concrete structures”. International Conference on Engineering Computational Technology, pp. 1-7.
Pandey, A.; Biswas, M., Samman, M., 1991. “Damage detection from changes in curvature mode shapes”. Journal of Sound and Vibration, vol. 145, n. 2, pp. 321-332.
Pandey, A., Biswas, M., 1994. “Damage detection in structures using changes in flexibility”. Journal of Sound and Vibration, vol. 169, n. 1, pp. 3-17.
Pimentel, R. L., Ferreira, G. S., Gonçalves, M. S., Nyawako, D. S., Reynolds, P., 2015. “Damage detection in concrete precast slabs: a quick assessment through modal tests”. MATEC Web of Conferences, vol. 24, EDP Sciences.
Principe, J. C., Euliano, N. R., Lefebvre, W. C., 1999. “Neural and adaptive systems: fundamentals through simulations with CD-ROM”. John Wiley & Sons, Inc.
Vapnik, V., 1995. “The nature of statistical learning theory”. Springer-Verlag.
Wen, C. M., Hung, S. L., Huang, C. S., Jan, J. C., 2007. “Unsupervised fuzzy neural networks for damage detection of structures”. Structural Control and Health Monitoring, vol. 14, n. 1, pp. 144-161.
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