Structural damage prognosis of three-dimensional large structural systems

References

• Al-Hussein, A. (2015). A novel technique for structural health assessment in the presence of nonlinearity (PhD dissertation). Department of Civil Engineering and Engineering Mechanics, University of Arizona, AZ, USA.
   Google Scholar
• Al-Hussein, A., & Haldar, A. (2015). Structural health assessment using extended and unscented Kalman filters. International Journal of Sustainable Materials and Structural Systems, 2, 52–76. doi:10.1504/IJSMSS.2015.078361
   Google Scholar
• Al-Hussein, A., & Haldar, A. (2016a). Unscented Kalman filter with unknown input and weighted global iteration for health assessment of large structural systems. Structural Control and Health Monitoring, 23(1), 156–175. doi:10.1002/stc.1764.
   Web of Science ®Google Scholar
• Al-Hussein, A., & Haldar, A. (2016b). Nonlinear system identification from noisy measurements. International Journal of Structural Engineering. Manuscript submitted for publication.
   Google Scholar
• ANSYS. (2014). Computer Software – Version 15.0. Canonsburg, PA: The Engineering Solutions Company.
   Google Scholar
• Azam, S. E., Chatzi, E., & Papadimitriou, C. (2015). A dual Kalman filter approach for state estimation via output-only acceleration measurements. Mechanical Systems and Signal Processing, 60–61, 866–886.10.1016/j.ymssp.2015.02.001
   Web of Science ®Google Scholar
• Azam, S. E., Chatzi, E., Papadimitriou, C., & Smyth, A. W. (2015). Experimental validation of Kalman-type filters for online and real-time state and input estimation. Journal of Vibration and Control. doi:10.1177/1077546315617672
   PubMed Web of Science ®Google Scholar
• Azam, S. E., Dertimanis, V., Chatzi, E., & Papadimitriou, C. (2015, May 25–27). Output only schemes for input-state-parameter estimation of linear systems. In Proceedings of UNCECOMP 2015. Greece: Crete.
   Google Scholar
• Bernal, D. (2013). Kalman filter damage detection in the presence of changing process and measurement noise. Mechanical Systems and Signal Processing, 39, 361–371.10.1016/j.ymssp.2013.02.012
   Web of Science ®Google Scholar
• Carmichael, D.G. (1979). The state estimation problem in experimental structural mechanics. In International Conference on Application of Statistics and Probability in Soil and Structural Engineering (3rd ed., pp. 802–815). Sydney.
   Google Scholar
• Chang, F. K. (1997). Structural health monitoring current status and perspectives. Lancaster, PA: Technomic Publishing.
   Google Scholar
• Chatzi, E. N., & Smyth, A. W. (2009). The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing. Structural Control and Health Monitoring, 16, 99–123.10.1002/stc.v16:1
   Web of Science ®Google Scholar
• Ching, J., & Beck, J. L. (2007). Real-time reliability estimation for serviceability limit states in structures with uncertain dynamic excitation and incomplete output data. Probabilistic Engineering Mechanics, 22, 50–62.10.1016/j.probengmech.2006.05.006
   Web of Science ®Google Scholar
• Clough, R. W., & Penzien, J. (2003). Dynamics of structures. (3rd ed.). Berkeley, CA: Computers and Structures.
   Google Scholar
• Das, A. K. (2012). Health assessment of three dimensional large structural systems using limited uncertain dynamic response information (PhD dissertation). Department of Civil Engineering and Engineering Mechanics, University of Arizona, AZ, USA.
   Google Scholar
• Das, A. K., & Haldar, A. (2012). Health assessment of three dimensional large structural systems – A novel approach. Life Cycle Reliability and Safety Engineering, 1(1), 1–14.
   Google Scholar
• Doebling, S. W., Farrar, C. R., Prime, M. B., & Shevitz, D.W. (1996). Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review (Report No. LA-13070-MS). Los Alamos, NM: Los Alamos National Laboratory.
   Google Scholar
• Farrar, C. R., Baker, W. E., Bell, T. M., Cone, K. M., Darling, T. W., Duffey, T. A., … Migliori, A. (1994). Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande (Report No. LA-12767-MS). Los Alamos, NM: Los Alamos National Laboratory.
   Google Scholar
• Haldar, A., Das, A. K., & Al-Hussein, A. (2013). Data analysis challenges in structural health assessment using measured dynamic responses. Advances in Adaptive Data Analysis, 5, 1350017-1–1350017-22.
   Google Scholar
• Haldar, A., Ling, X., & Wang, D. (1998). Use of SI for damage evaluation at the element level. Paper presented at the meeting of Structural Engineering World Congress, Paper No: T216-1, San Francisco, CA.
   Google Scholar
• Haldar, A., Martinez-Flores, R., & Katkhuda, H. (2008). Crack detection in existing structures using noise-contaminated dynamic responses. Theoretical and Applied Fracture Mechanics, 50, 74–80.10.1016/j.tafmec.2008.04.007
   Web of Science ®Google Scholar
• Hoshiya, M., & Saito, E. (1984). Structural identification by extended Kalman filter. Journal of Engineering Mechanics, 110, 1757–1770.10.1061/(ASCE)0733-9399(1984)110:12(1757)
   Web of Science ®Google Scholar
• Jiang, X., & Adeli, H. (2007). Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings. International Journal for Numerical Methods in Engineering, 71, 606–629.10.1002/(ISSN)1097-0207
   Web of Science ®Google Scholar
• Julier, S.J. (2002). The scaled unscented transformation. In Proceedings of the American Control Conference (IEEE Cat. No.CH37301) (Vol. 6, pp. 4555–4559). Anchorage, AK.
   Google Scholar
• Julier, S. J. (2003). The spherical simplex unscented transformation. In Proceedings of American Control Conference (Vol. 3, 2430–2434).
   Google Scholar
• Julier, S. J., Uhlmann, J. K., & Durrant-Whyte, H. F. (1995). A new approach for filtering nonlinear systems. In Proceedings of the American Control Conference (Vol. 3, pp. 1628–1632). Washington, DC: Seattle.
   Google Scholar
• Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 35–45.10.1115/1.3662552
   Google Scholar
• Katkhuda, H., & Haldar, A. (2008). A novel health assessment technique with minimum information. Structural Control and Health Monitoring, 15, 821–838.10.1002/stc.v15:6
   Web of Science ®Google Scholar
• Katkhuda, H., Martinez-Flores, R., & Haldar, A. (2005). Health assessment at local level with unknown input excitation. Journal of Structural Engineering, 131, 956–965.10.1061/(ASCE)0733-9445(2005)131:6(956)
   Web of Science ®Google Scholar
• Koh, C. G., See, L. M., & Balendra, T. (1991). Estimation of structural parameters in time domain: A substructure approach. Earthquake Engineering and Structural Dynamics, 20, 787–801.10.1002/(ISSN)1096-9845
   Web of Science ®Google Scholar
• Koh, C. G., See, L. M., & Balendra, T. (1995). Determination of storey stiffness of three-dimensional frame buildings. Engineering Structures, 17, 179–186.10.1016/0141-0296(95)00055-C
   Web of Science ®Google Scholar
• Leven, W. F., & Lanterman, A. D. (2009). Unscented Kalman filters for multiple target tracking with symmetric measurement equations. IEEE Transactions on Automatic Control, 54, 370–375.10.1109/TAC.2008.2008327
   Web of Science ®Google Scholar
• Ling, X.. (2000). Linear and nonlinear time domain system identification at element level for structural systems with unknown excitations (PhD dissertation). Department of Civil Engineering and Engineering Mechanics, University of Arizona, AZ, USA.
   Google Scholar
• Liu, H., Yang, Z., & Gaulke, M. S. (2005). Structural identification and finite element modeling of a 14-story office building using recorded data. Engineering Structures, 27, 463–473.10.1016/j.engstruct.2004.11.012
   Web of Science ®Google Scholar
• Loh, C.-H., & Tsaur, Y.-H. (1988). Time domain estimation of structural parameters. Engineering Structures, 10, 95–105.10.1016/0141-0296(88)90035-1
   Web of Science ®Google Scholar
• Lourens, E., Papadimitriou, C., Gillijns, S., Reynders, E., De Roeck, G., & Lombaert, G. (2012). Joint input-response estimation for structural systems based on reduced-order models and vibration data from a limited number of sensors. Mechanical Systems and Signal Processing, 29, 310–327.10.1016/j.ymssp.2012.01.011
   Web of Science ®Google Scholar
• Maes, K., Van Nimmen, K., Lourens, E., Rezayat, A., Guillaume, P., De Roeck, G., & Lombaert, G. (2016, July 5–8). Validation of joint input-state estimation for force identification and response estimation in structural dynamics. 8th European Workshop on Structural Health Monitoring (EWSHM 2016), Bilbao, Spain.
   Google Scholar
• Mariani, S., & Ghisi, A. (2007). Unscented Kalman filtering for nonlinear structural dynamics. Nonlinear Dynamics, 49, 131–150.10.1007/s11071-006-9118-9
   Web of Science ®Google Scholar
• Naets, F., Croes, J., & Desmet, W. (2015). An online coupled state/input/parameter estimation approach for structural dynamics. Computer Methods in Applied Mechanics and Engineering, 283, 1167–1188.10.1016/j.cma.2014.08.010
   Web of Science ®Google Scholar
• Naets, F., Pastorino, R., Cuadrado, J., & Desmet, W. (2014). Online state and input force estimation for multibody models employing extended Kalman filtering. Multibody System Dynamics, 32, 317–336.10.1007/s11044-013-9381-8
   Web of Science ®Google Scholar
• Osornio-Rios, R. A., Amezquita-Sanchez, J. P., Romero-Troncoso, R. J., & Garcia-Perez, A. (2012). MUSIC-ANN analysis for locating structural damages in a truss-type structure by means of vibrations. Computer-Aided Civil and Infrastructure Engineering, 27(9), 687–698.10.1111/mice.2012.27.issue-9
   Web of Science ®Google Scholar
• Paz, M., & Leigh, M. (2004). Structural dynamics: Theory and computation. (5th ed.). Norwell, MA: Klumer Academic Publishers.10.1007/978-1-4615-0481-8
   Google Scholar
• Raghavendrachar, M., & Aktan, A. E. (1992). Flexibility of multi reference impact testing for bridge diagnostics. Journal of Structural Engineering, 118, 2186–2203.10.1061/(ASCE)0733-9445(1992)118:8(2186)
   Web of Science ®Google Scholar
• Rao, S. K., & Babu, V. S. (2008). Unscented Kalman filter with application to bearings-only passive manoeuvring target tracking. In International Conference on Signal Processing, Communications and Networking, ICSCN ’08 (pp. 219–224). Chennai.
   Google Scholar
• Toki, K., Sato, T., & Kiyono, J. (1989). Identification of structural parameters and input ground motion from response time histories. Journal of Structural Engineering/Earthquake Engineering, 6, 413–421.
   Google Scholar
• van der Merwe, R., & Wan, E. A. (2001). The square-root unscented Kalman filter for state and parameter-estimation. In Processing of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’01) (Vol. 6, pp. 3461–3464). Salt Lake City, UT.
   Google Scholar
• Vo, P. H., & Haldar, A. (2003). Post-processing of linear accelerometer data in structural identification. Journal of Structural Engineering, 30, 123–130.
   Google Scholar
• Wang, D., & Haldar, A. (1994). Element‐level system identification with unknown input. Journal of Engineering Mechanics, 120, 159–176.
   Web of Science ®Google Scholar
• Wang, D., & Haldar, A. (1997). System identification with limited observations and without input. Journal of Engineering Mechanics, 123, 504–511.10.1061/(ASCE)0733-9399(1997)123:5(504)
   Web of Science ®Google Scholar
• Wu, M., & Smyth, A. W. (2007). Application of the unscented Kalman filter for real-time nonlinear structural system identification. Structural Control and Health Monitoring, 14, 971–990.10.1002/(ISSN)1545-2263
   Web of Science ®Google Scholar
• Yun, C.-B., & Shinozuka, M. (1980). Identification of nonlinear structural dynamic systems. Journal of Structural Mechanics, 8, 187–203.10.1080/03601218008907359
   Web of Science ®Google Scholar
• Zhang, D. (2010). Controlled substructure identification for shear structures (PhD Dissertation). Department of Civil Engineering, University of Southern California, CA, USA.
   Google Scholar
• Zhang, H., Dai, G., Sun, J., & Zhao, Y. (2013). Unscented Kalman filter and its nonlinear application for tracking a moving target. Optik – International Journal for Light and Electron Optics, 124, 4468–4471.10.1016/j.ijleo.2013.03.013
   Google Scholar