Refrences:
1.Adeli, H. Neural networks in civil engineering: 1989- 2000", Comput. Civ. Infrastruct. Eng., 16(2), pp. 126- 142 (2001).
2. Chang, P.C. and Liu, S.C. Recent research in nondestructive evaluation of civil infrastructures", J. Mater. Civ. Eng., 15(3), pp. 298-304 (2003).
3. Fan, W. and Qiao, P. Vibration-based damage identi _cation methods: a review and comparative study", Struct. Heal. Monit., 10(1), pp. 83-111 (2011). 4. Lynch, J.P. and Loh, K.J. A summary review of wireless sensors and sensor networks for structural health monitoring", Shock Vib. Dig., 38(2), pp. 91-130 (2006). 5. Doebling, S.W., Farrar, C.R., and Prime, M.B. A summary review of vibration-based damage identi_- cation methods", Shock Vib. Dig., 30(2), pp. 91-105 (1998). 6. Yan, Y.J., Cheng, L.,Wu, Z.Y., and Yam, L.H. Development in vibration-based structural damage detection technique", Mech. Syst. Signal Process., 21(5), pp. 2198-2211 (2007). 7. Montalvao, D., Maia, N.M.M., and Ribeiro, A.M.R. A review of vibration-based structural health monitoring with special emphasis on composite materials", Shock Vib. Dig., 38(4), pp. 295-326 (2006). 8. Goyal, D. and Pabla, B.S. The vibration monitoring methods and signal processing techniques for structural health monitoring: A review", Arch. Comput. Methods Eng., 23(4), pp. 585-594 (2016). https://link.springer.com/article/10.1007/s11831-015- 9145-0 9. Chang, P.C., Flatau, A., and Liu, S.C. Review paper: Health monitoring of civil infrastructure", Struct. Heal. Monit., 2(3), pp. 257-267 (2003). 10. Qarib, H. and Adeli, H. Recent advances in health monitoring of civil structures", Sci. Iran., 21(6), pp. 1733-1742 (2014). 11. Loh, C.-H., Wu, T.-C., and Huang, N.E. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses", Bull. Seismol. Soc. Am., 91(5), pp. 1339-1357 (2001). 12. Yang, J., Lei, Y., Lin, S., and Huang, N. Hilbert- Huang based approach for structural damage detection", J. Eng. Mech., 130, pp. 85-95 (January 2004). 13. Zhang, R.R., Ma, S., Safak, E., and Hartzell, S. Hilbert-Huang transform analysis of dynamic and earthquake motion recordings", J. Eng. Mech., 129(8), pp. 861-875 (2003). 14. Hou, Z., Noori, M., and Amand, R. St. Wavelet-based approach for structural damage detection", J. Eng. Mech., 126(7), pp. 677-683 (2000). 15. Li, H., Zhang, Y., and Zheng, H. Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings", J. Mech. Sci. Technol., 23(2), pp. 291-301 (2009). 16. Amini Tehrani, H., Bakhshi, A., and Akhavat, M. An e_ective approach for structural damage localization in exural members based on generalized S-transform", Sci. Iran. (In Press). http://scientiairanica.sharif.edu/article 20019.html 17. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., and Liu, H.H. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis", Proc. R. Soc. London A Math. Phys. Eng. Sci., 454(1971), pp. 903-995 (1998). 18. Huang, N., Long, S., and Shen, Z. The mechanism for frequency downshift in nonlinear wave evolution", Adv. Appl. Mech., 32, pp. 59-117 (1996). https://www.sciencedirect.com/science/article/pii/S0 065215608700760 19. Chen, B., Zhao, S., and Li, P. Application of Hilbert- Huang transform in structural health monitoring: A state-of-the-art review", Math. Probl. Eng., 2014(7), pp. 1-22 (2014). 20. Huang, N.E., Shen, Z., and Long, S.R. A new view of nonlinear water waves: The hilbert spectrum1", Annu. Rev. Fluid Mech., 31(1), pp. 417-457 (1999). 21. Vincent, H.T., Hu, S.L.J., and Hou, Z. Damage detection using empirical mode decomposition method and a comparison with wavelet analysis", in 2nd International Workshop on Structural Health Monitoring, Stanford University, pp. 891-900 (1999). 22. Wu, Z. and Huang, N.E. A study of the characteristics of white noise", R. Soc., 460, pp. 1597-1611 (2004). 23. Wu, Z. and Huang, N.E., Ensemble Empirical Mode Decomposition, World Sci. Publ., 1(1), pp. 1-41 (2009). 24. Aied, H., Gonz_alez, A., and Cantero, D. Identi_cation of sudden sti_ness changes in the acceleration response of a bridge to moving loads using ensemble empirical mode decomposition", Mech. Syst. Signal Process., 66- 67, pp. 314-338 (2016). 25. Moser, P. and Moaveni, B. Environmental e_ects on the identi_ed natural frequencies of the Dowling Hall Footbridge", Mech. Syst. Signal Process., 25(7), pp. 2336-2357 (2011). 26. Nagarajaiah, S. and Basu, B. Output only modal identi_cation and structural damage detection using time frequency & wavelet techniques", Earthq. Eng. Eng. Vib., 8(4), pp. 583-605 (2009). 27. Bishop, C.M., Neural Networks for Pattern Recognition, Oxford University Press, Inc. (1995). 28. Suresh, S., Omkar, S.N., Ganguli, R., and Mani, V. Identi_cation of crack location and depth in a cantilever beam using a modular neural network approach", Smart Mater. Struct., 13(4), pp. 907-915 (2004). 29. Fang, X., Luo, H., and Tang, J. Structural damage detection using neural network with learning rate improvement", Comput. Struct., 83(25-26), pp. 2150- 2161 (2005). 30. Xu, B., Wu, Z., Chen, G., and Yokoyama, K. Direct identi_cation of structural parameters from dynamic responses with neural networks", Eng. Appl. Artif. Intell., 17, pp. 931-943 (2004). 31. Saadat, S., Buckner, G.D., Furukawa, T., and Noori, M.N. An intelligent parameter varying (IPV) approach for non-linear system identi_cation of base excited structures", Int. J. Non. Linear. Mech., 39(6), pp. 993-1004 (2004). 32. Bandara, R.P., Chan, T.H.T., and Thambiratnam, D.P. Structural damage detection method using frequency response functions", Struct. Heal. Monit., 13(4), pp. 418-429 (Feb. 2014). 33. Ra_ei, M.H. and Adeli, H. A novel unsupervised deep learning model for global and local health condition assessment of structures", Eng. Struct., 156, pp. 598- 607 (2018). 34. Entezami, A., Shariatmadar, H., and Karamodin, A. An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods", Sci. Iran. (In Press). http://scientiairanica.sharif.edu/article 20641 0.html 35. Amezquita-Sanchez, J.P. and Adeli, H. Feature extraction and classi_cation techniques for health monitoring of structures", Sci. Iran. Trans. A, Civ. Eng., 22(6), p. 1931 (2015). 36. Huang, N.E. and Shen, S.S., Hilbert-Huang Transform and Its Applications, (World Scienti_c, 5 (2005). 37. Huang, N.E., Computing Instantaneous Frequency by Normalizing Hilbert Transform, Patent 6901353, U.S. Patent and Trademark O_., Washington, D.C. (2005). 38. Bahar, O. and Ramezani, S. Enhanced Hilbert-Huang transform and its application to modal identi_cation", Struct. Des. Tall Spec. Build., 23(4), pp. 239-253 (2014). 39. Wang, T., Zhang, M., Yu, Q., and Zhang, H. Comparing the applications of EMD and EEMD on timefrequency analysis of seismic signal", J. Appl. Geophys., 83, pp. 29-34 (2012). 40. Jiang, S., Wu, S., and Dong, L. A time-domain structural damage detection method based on improved multiparticle swarm coevolution optimization algorithm", Math. Probl. Eng., 2014, p. 11 (2014). 41. Friswell, M.I., Penny, J.E.T., and Garvey, S.D. A combined genetic and eigensensitivity algorithm for the location of damage in structures", Comput. Struct., 69(5), pp. 547-556 (1998).
)