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Research Article

Bridge damage analysis under joint environmental and operational variability

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Received 16 Feb 2023, Accepted 12 Jul 2023, Published online: 04 Aug 2023

References

  • An, Y., Chatzi, E., Sim, S. H., Laflamme, S., Blachowski, B., & Ou, J. (2019). Recent progress and future trends on damage identification methods for bridge structures. Structural Control and Health Monitoring, 26(10), e2416. doi:10.1002/stc.2416
  • Azim, M. R., & Gül, M. (2019). Damage detection of steel girder railway bridges utilizing operational vibration response. Structural Control and Health Monitoring, 26(11), e2447. doi:10.1002/stc.2447
  • Bisheh, H. B., & Amiri, G. G. (2023). Structural damage detection based on variational mode decomposition and kernel PCA-based support vector machine. Engineering Structures, 278, 115565. doi:10.1016/j.engstruct.2022.115565
  • Code, P. (2007). Eurocode 3: Design of Steel Structures-Part 1-2: General Rules-Structural Fire Design. London: European Committee for Standardisation.
  • Cremona, C., & Santos, J. (2018). Structural health monitoring as a big-data problem. Structural Engineering International, 28(3), 243–254. doi:10.1080/10168664.2018.1461536
  • Delgadillo Rick, M., & Casas Joan, R. (2019). SHM of Bridges by Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Clustering. In Proceedings of the Twelfth International Workshop on Structural Health Monitoring, September 10–12, Stanford University, USA.
  • Delgadillo Rick, M., & Casas Joan, R. (2022). Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms. Structural Control and Health Monitoring, 29(8), e2966. doi:10.1002/stc.2966
  • Delgadillo Rick, M., & Casas Joan, R. (2021). A combined kernel-PCA with clustering analysis for bridge damage detection under changing environmental conditions. In Life-Cycle Civil Engineering: Innovation, Theory and Practice. CRC Press. pp. 1362–1370.
  • Delgadillo, R. M., Tenelema, F. J., & Casas, J. R. (2023). Marginal Hilbert spectrum and instantaneous phase difference as total damage indicators in bridges under operational traffic loads. Structure and Infrastructure Engineering, 19(6), 824–844. doi:10.1080/15732479.2021.1982994
  • Delgadillo, R. M. (2022). Development of a machine learning based methodology for bridge health monitoring [PhD Thesis]. UPC-BarcelonaTech, Barcelona, Spain.
  • Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544. doi:10.1109/TSP.2013.2288675
  • Entezami, A., Sarmadi, H., Behkamal, B., & Mariani, S. (2020). Big data analytics and structural health monitoring: A statistical pattern recognition-based approach. Sensors, 20(8), 2328. doi:10.3390/s20082328
  • Fallahian, M., Khoshnoudian, F., & Meruane, V. (2018). Ensemble classification method for structural damage assessment under varying temperature. Structural Health Monitoring, 17(4), 747–762. doi:10.1177/1475921717717311
  • Fallahian, M., Khoshnoudian, F., Talaei, S., Meruane, V., & Shadan, F. (2018). Experimental validation of a deep neural network—sparse representation classification ensemble method. The Structural Design of Tall and Special Buildings, 27(15), e1504. doi:10.1002/tal.1504
  • Figueiredo, E., Radu, L., Westgate, R., Brownjohn, J., Cross, E., Worden, K., & Farrar, C. (2012). Applicability of a Markov-Chain Monte Carlo method for damage detection on data from the Z-24 and Tamar suspension bridges. In Proceedings of the 6th European Workshop-Structural Health Monitoring 2012, EWSHM 2012, pp. 747–754.
  • Huang, N. E., Long, S. R., & Shen, Z. (1996). The mechanism for frequency downshift in nonlinear wave evolution. Advances in Applied Mechanics, 32, 59–117.
  • Huang, J. Z., Li, D. S., Li, H. N., Song, G. B., & Liang, Y. (2018). Damage identification of a large cable‐stayed bridge with novel cointegrated Kalman filter method under changing environments. Structural Control and Health Monitoring, 25(5), e2152. doi:10.1002/stc.2152
  • Jolliffe, I. (2002). Principal component analysis. 2nd. ed. New York: Springer-Verlag.
  • Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 374(2065), 20150202. doi:10.1098/rsta.2015.0202
  • Kunwar, A., Jha, R., Whelan, M., & Janoyan, K. (2013). Damage detection in an experimental bridge model using Hilbert–Huang transform of transient vibrations. Structural Control and Health Monitoring, 20(1), 1–15. doi:10.1002/stc.466
  • Li, J., Zhu, X., & Guo, J. (2022). Bridge modal identification based on successive variational mode decomposition using a moving test vehicle. Advances in Structural Engineering, 25(11), 2284–2300. doi:10.1177/13694332221092678
  • Liu, A., Wang, L., Bornn, L., & Farrar, C. (2019). Robust structural health monitoring under environmental and operational uncertainty with switching state-space autoregressive models. Structural Health Monitoring, 18(2), 435–453. doi:10.1177/1475921718757721
  • Neves, A. C. (2020). Structural health monitoring of bridges: Data-based damage detection method using machine learning [Doctoral dissertation]. KTH Royal Institute of Technology, Sweden.
  • Mousavi, A. A., Zhang, C., Masri, S. F., & Gholipour, G. (2022). Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: A model steel truss bridge case study. Structural Health Monitoring, 21(3), 887–912. doi:10.1177/14759217211013535
  • Neves, A. C., González, I., Karoumi, R., & Leander, J. (2021). The influence of frequency content on the performance of artificial neural network–based damage detection systems tested on numerical and experimental bridge data. Structural Health Monitoring, 20(3), 1331–1347. doi:10.1177/1475921720924320
  • Ooijevaar, T. H. (2014). Vibration based structural health monitoring of composite skin-stiffener structures [PhD Thesis]. University of Twente, The Netherlands.
  • Pathirage, C. S. N., Li, J., Li, L., Hao, H., Liu, W., & Ni, P. (2018). Structural damage identification based on autoencoder neural networks and deep learning. Engineering Structures, 172, 13–28. doi:10.1016/j.engstruct.2018.05.109
  • Peeters, B., Maeck, J., & De Roeck, G. (2001). Vibration-based damage detection in civil engineering: Excitation sources and temperature effects. Smart Materials and Structures, 10(3), 518–527. doi:10.1088/0964-1726/10/3/314
  • Priestley, M. N., Seible, F., & Calvi, G. M. (1996). Seismic design and retrofit of bridges. John Wiley & Sons.
  • Santos, A., Santos, R., Silva, M., Figueiredo, E., Sales, C., & Costa, J. C. (2017). A global expectation–maximization approach based on memetic algorithm for vibration-based structural damage detection. IEEE Transactions on Instrumentation and Measurement, 66(4), 661–670. doi:10.1109/TIM.2017.2663478
  • Sierra Pérez, J. (2014). Smart aeronautical structures: development and experimental validation of a structural health monitoring system for damage detection [Doctoral dissertation]. Aeronauticos.
  • Skowroński, W., Włóka, A., & Chmiel, R. (2014). Modelling of strength processes of S235JR steel at increased temperature (Modelowanie procesów wytrzymałościowych stali S235JR w podwyższonej temperaturze). Structure & Environment, 6(3), 32–37.
  • Soo Lon Wah, W., Chen, Y. T., Roberts, G. W., & Elamin, A. (2018). Separating damage from environmental effects affecting civil structures for near real-time damage detection. Structural Health Monitoring, 17(4), 850–868. doi:10.1177/1475921717722060
  • Tatsis, K., & Chatzi, E. (2019). A numerical benchmark for system identification under operational and environmental variability. In 8th International Operational Modal Analysis Conference (IOMAC 2019).
  • Tenelema, F. J. (2020). Bridge Damage Identification under operational and environmental variability [Master’s Thesis]. UPC-BarcelonaTech, Barcelona, Spain.
  • Tenelema Muñoz, F. J., Delgadillo Ayala, R. M., & Casas Rius, J. R. (2021). Damage detection of bridges considering environmental variability using Hilbert-Huang Transform and Principal Component Analysis. In Proceedings of the International Conference on Structural Health Monitoring of Intelligent Infrastructure. Advanced Research and Real-World Applications. SHMII-10, Porto, Portugal, pp. 529–536.
  • Witting, M., & Klein, M. (1996). Modal selection by means of effective modal masses and effective modal forces-An application example (launch vehicles). In ESA International Conference on Spacecraft Structures, Materials and Mechanical Testing, Noordwijk, Netherlands, pp. 715–722.
  • Yang, C., & Liu, Y. (2021). Detecting the damage of bridges under changing environmental conditions using the characteristics of the nonlinear narrow dimension of damage features. Mechanical Systems and Signal Processing, 159, 107842. doi:10.1016/j.ymssp.2021.107842
  • Yu, L., Zhu, J. H., & Yu, L. L. (2013). Structural damage detection in a truss bridge model using fuzzy clustering and measured FRF data reduced by principal component projection. Advances in Structural Engineering, 16(1), 207–217. doi:10.1260/1369-4332.16.1.207
  • Zang, J. G., Huang, H.-B., Sun, Z.-G., & Wang, D. S. (2023). Subdomain principal component analysis for damage detection of structures subjected to changing environments. Engineering Structures, 288, 116265. doi:10.1016/j.engstruct.2023.116265
  • Zhu, Y. C., Xiong, W., & Song, X. D. (2022). Structural performance assessment considering both observed and latent environmental and operational conditions: A Gaussian process and probability principal component analysis method. Structural Health Monitoring, 21(6), 2531–2546. 14759217211062099. doi:10.1177/14759217211062099

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