Damage detection in frame structures using noisy accelerometers and Damage Load Vectors (DLV)

Document Type : Article


Department of Civil Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia.


In the area of damage detection, there have been many notable methods introduced in the past years. Damage Load Vectors (DLV) is among the most powerful methods, which computes a set of load vectors from variations in flexibility matrices of a frame in the undamaged and damaged conditions. These flexibility matrices are derived from acceleration responses of the frame which can be captured using accelerometers. The DLV method then scrutinizes this shift among the flexibility matrices, which ultimately enables locating the damaged member(s). This study holistically conducted seven experimental tests, with seven damage scenarios of a test frame installed on a semi-harmonic shaking table. The DLV method was subsequently employed to locate the damaged members using recorded frame vibration data obtained from ‘noisy’ accelerometers positioned on the frame at eight predefined locations. The Eigen Realization Algorithm (ERA) alongside Pandy’s recommendations were adapted herein to facilitate generation of accurate flexibility matrices derived from the noisy accelerometers. The outcome is very encouraging with accurate identification of damaged members in all seven damage scenarios without any ‘positive-false’ and ‘negative-false’ findings. Additionally, there is a decrease (from 0.045 to 0.289) in the accuracy of WSI index when the number of damaged members is increased.


Main Subjects

1. Naderpour, H., Ezzodin, A., Kheyroddin, A., and Amiri, G.G. Signal processing based damage detection of concrete bridge piers subjected to consequent excitations", J. Vibroengineering, 19(3), pp. 2080{ 2089 (2017). 2. Naderpour, H., Sharbatdar, M.K., and Khademian, F. Damage detection of reinforced concrete shear wallsusing mathematical transformations", J. Structral Constr. Eng., 3(4), pp. 79{96 (2017). 3. George, R.C., Posey, J., Gupta, A., Mukhopadhyay, S., and Mishra, S.K. Damage detection in railway bridges under moving train load", Model Validation and Uncertainty Quanti_cation, 3(1), pp. 349{354 (2017). 4. Soman, R., Majewska, K., Mieloszyk, M., Malinowski, P., and Ostachowicz, W. Application of Kalman Filter based neutral axis tracking for damage detection in composites structures", Compos. Struct., 184(1), pp. 66{77 (2018). 5. Betti, M., Facchini, L., and Biagini, P. Damage detection on a three-storey steel frame using arti_cial neural networks and genetic algorithms", Meccanica, 50(3), pp. 875{886 (2015). 6. Maia, N.M.M. and Montalvaao e Silva, J.M. Chapter 4 - modal analysis identi_cation methods", In Theoretical and Experimental Modal Analysis, 2nd Edn. pp. 185{264, Research Studies Press, Hertfordshire, England (1987). 7. Sikorsky, C., Stubbs, N., and Kim, B.H. Local damage detection using incomplete modal data", In Prooceedings of the 20th International Modal Analysis Conference, pp. 435{441 (2002). 8. Castro-Triguero, R., Murugan, S., Gallego, R., and Friswell, M.I. Robustness of optimal sensor placement under parametric uncertainty", Mech. Syst. Signal Process., 41(1{2), pp. 268{287 (2013). 9. Ojeda, A.P., MATLAB Implementation of an Operational Modal Analysis Technique for Vibration-Based Structural Health Monitoring, Massachusetts Institute of Technology (MIT) (2012). 10. Horiuchi, K.K.P., Structural Health Monitoring with the Modal Strain Energy, California State University (2014). 11. Gao, Y. and Spencer, B.F. Damage localization under ambient vibration using changes in exibility", Earthq. Eng. Eng. Vib., 1(1), pp. 136{144 (2002). 1784 I. Toloue et al./Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 1776{1785 12. Gao, Y., Spencer, B.F., and Bernal, D. Experimental veri_cation of the exibility-based damage locating vector method", J. Eng. Mech., 133(10), pp. 1043{ 1049 (2007). 13. Pandey, A. and Biswas, M. Damage detection in structures uding changes in exibility", J. Sound Vib., 169(1), pp. 3{17 (1994). 14. Alvandi, A. and Cremona, C. Assessment of vibration-based damage identi_cation techniques", J. Sound Vib., 292(1{2), pp. 179{202 (2006). 15. Zhang, W., Sun, L., and Zhang, L. Local damage identi_cation method using _nite element model updating based on a new wavelet damage function", Adv. Struct. Eng., 21(10), pp. 1482{1494 (2018). 16. Weng, S., Zhu, H., Gao, R., Li, J., and Chen, Z. Identi_cation of free-free exibility for model updating and damage detection of structures", J. Aerosp. Eng., 31(3), p. 4018017 (2018). 17. Bernal, D. Extracting exibility matrices from State- Space realizations", In COST F3 Conference, pp. 127{ 135 (2000). 18. Anh, T.V., Enhacement to the Damage Locating Vector Method for Structural Health Monitoring, National University of Singapore (2009). 19. Juang, J.-N. and Pappa, R.S. An eigensystem realization algorithm for modal parameter identi_cation and model reduction", J. Guid. Control. Dyn., v8(5), pp. 620{627 (1985). 20. Singh, M., Elbadawy, M., and Bisht, S. Dynamic strain response measurement-based damage identi_- cation in structural frames", Struct. Control Heal. Monit., 25(7), p. e2181 (2018). 21. Camacho Navarro, J., Ruiz, M., Villamizar, R., Mujica, L., and Quiroga, J. Features of cross-correlation analysis in a data-driven approach for structural damage assessment", Sensors, 18(5), p. 1571 (2018). 22. Bernal, D. Load vectors for damage localization", J. Eng. Mech., 128(1), pp. 7{14 (2002). 23. Monajemi, H., Razak, H.A., and Ismail, Z. Damage detection in frame structures using damage locating vectors", Measurement, 46(9), pp. 3541{3548 (2013). 24. Jung, H.Y., Sung, S.H., and Jung, H.J. Experimental validation of normalized uniform load surface curvature method for damage localization", Sensors (Switzerland), 15(10), pp. 26315{26330 (2015). 25. Bernal, D. Analytical techniques for damage detection and localization for assessing and monitoring civil infrastructures", In Sensor Technologies for Civil Infrastructures, pp. 67{92, Elsevier Press, Netherlands (2014). 26. Gunes, B. Structural health monitoring and damage assessment Part II: Application of the Damage Locating Vector (DLV) method to the ASCE benchmark structure experimental data", Int. J. Phys. Sci., 7(9), pp. 1509{1515 (2012). 27. Bernal, D. Load vectors for damage location in systems identi_ed from operational loads," J. Eng. Mech., 136(1), pp. 31{39 (2010). 28. Wang, Z. and Ong, K.C.G. Multivariate statistical approach to structural damage detection", J. Eng. Mech., 136(1), pp. 12{22 (2010). 29. Wang, Y.P., Chang, M.L., and Chang, J.G. Damage localization of structures identi_ed with a deterministic-stochastic model", Appl. Mech. Mater., 501(1), pp. 856{860 (2014). 30. Blachowski, B., An, Y., Spencer, B.F., and Ou, J. Axial strain accelerations approach for damage localization in statically determinate truss structures", Comput. Civ. Infrastruct. Eng., 32(4) pp. 304{318 (2017).