Data-driven damage identification technique for steel truss railroad bridges utilizing principal component analysis of strain response

References

• ASCE. (2017). Infrastructure report card: Rail. Retrieved from https://www.infrastructurereportcard.org/cat-item/rail/.
   Google Scholar
• Azim, M. R., & Gül, M. (2019). Damage detection of steel girder railway bridges utilizing operational vibration response. Structural Control & Health Monitoring, 26(11), e2447. doi:10.1002/stc.2447
   Web of Science ®Google Scholar
• Azim, M. R., & Gül, M. (2020a). Damage detection of steel truss railway bridges using operational vibration data. Journal of Structural Engineering, 146(3), 04020008. doi:10.1061/(ASCE)ST.1943-541X.0002547
   Web of Science ®Google Scholar
• Azim, M. R., & Gül, M. (2020b). Damage detection of railway bridges using operational vibration data: Theory and experimental verifications. Structural Monitoring and Maintenance, 7(2), 149–166. doi:10.12989/smm.2020.7.2.149.
   Web of Science ®Google Scholar
• Banerji, P., & Chikermane, S. (2011). Structural health monitoring of a steel railway bridge for increased axle loads. Structural Engineering International, 21(2), 210–217. doi:10.2749/101686611X12994961034570
   Web of Science ®Google Scholar
• Bisheh, H. B., Amiri, G. G., Nekooei, M., & Darvishan, E. (2019). Damage detection of a cable-stayed bridge using feature extraction and selection methods. Structure and Infrastructure Engineering, 15(9), 1165–1177. doi:10.1080/15732479.2019.1599964
   Web of Science ®Google Scholar
• Bowe, C., Quirke, P., Cantero, D., & O’Brien, E. J. (2015). Drive-by structural health monitoring of railway bridges using train mounted accelerometers. In 5th ECCOMAS thematic conference on computational methods in structural dynamics and earthquake engineering, Greece.
   Google Scholar
• Catbas, F. N., Gokce, H. B., & Gül, M. (2012). Nonparametric analysis of structural health monitoring data for identification and localization of changes: Concept, lab, and real-life studies. Structural Health Monitoring: An International Journal, 11(5), 613–614. doi:10.1177/1475921712451955
   Web of Science ®Google Scholar
• Catbas, F. N., Gül, M., Gokce, H. B., Zaurin, R., Frangopol, D. M., & Grimmelsman, K. A. (2014). Critical issues, condition assessment and monitoring of heavy movable structures: Emphasis on movable bridges. Structure and Infrastructure Engineering, 10(2), 261–276. doi:10.1080/15732479.2012.744060
   Web of Science ®Google Scholar
• Chang, K. C., Kim, C. W., & Kawatani, M. (2014). Feasibility investigation for a bridge damage identification method through moving vehicle laboratory experiment. Structure and Infrastructure Engineering, 10(3), 328–345. doi:10.1080/15732479.2012.754773
   Web of Science ®Google Scholar
• CSI. (2014). Analysis reference manual for SAP2000, ETABS, SAFE, and CSiBridge. Computers and Structures Inc., Berkeley, California, USA.
   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 (A-13070-MS). Los Alamos National Laboratory, USA.
   Google Scholar
• dos Santos, F. L. M., Peeters, B., Lau, J., Desmet, W., & Goes, L. C. S. (2015). The use of strain gauges in vibration-based damage detection. Journal of Physics: Conference Series, 628, 012119. doi:10.1088/1742-6596/628/1/012119
   Google Scholar
• Feng, D., & Feng, M. Q. (2015). Model updating of railway bridge using in situ dynamic displacement measurement under trainloads. Journal of Bridge Engineering, 20(12), 04015019. doi:10.1061/(ASCE)BE.1943-5592.0000765
   Web of Science ®Google Scholar
• George, R. C., Mishra, S. K., & Dwivedi, M. (2018). Mahalanobis distance among the phase portraits as damage feature. Structural Health Monitoring, 17(4), 869–887. doi:10.1177/1475921717722743
   Web of Science ®Google Scholar
• George, R. C., Posey, J., Gupta, A., Mukhopadhyay, S., & Mishra, S. K. (2017). Damage detection in railway bridges under moving train load. Proceedings of the Society for Experimental Mechanics Series. Model Validation and Uncertainty Quantification, 3, 349–354.
   Google Scholar
• Goi, Y., & Kim, C. W. (2017). Damage detection of a truss bridge utilizing a damage indicator from multivariate autoregressive model. Journal of Civil Structural Health Monitoring, 7(2), 153–162. doi:10.1007/s13349-017-0222-y
   Web of Science ®Google Scholar
• Gu, J., Gül, M., & Wu, X. (2017). Damage detection under varying temperature using Artificial Neural Networks. Structural Control and Health Monitoring, 24(11), e1998. doi:10.1002/stc.1998
   Web of Science ®Google Scholar
• Hester, D., & González, A. (2015). A bridge-monitoring tool based on bridge and vehicle accelerations. Structure and Infrastructure Engineering, 11(5), 619–637. doi:10.1080/15732479.2014.890631
   Web of Science ®Google Scholar
• Hong, W., Cao, Y., & Wu, Z. (2016). Strain-based damage assessment method for bridges under moving vehicular load using long-gauge strain sensing. Journal of Bridge Engineering, 21(10), 04016059. doi:10.1061/(ASCE)BE.1943-5592.0000933
   Web of Science ®Google Scholar
• Jana, D., Mukhopadhyay, S., & Chaudhuri, S. R. (2019). Fisher information-based optimal input locations for modal identification. Journal of Sound and Vibration, 459, 114833. doi:10.1016/j.jsv.2019.06.040
   Web of Science ®Google Scholar
• Kopsaftopoulos, F. P., & Fassois, S. D. (2010). Vibration based health monitoring for a lightweight truss structure: Experimental assessment of several statistical time series methods. Mechanical Systems and Signal Processing, 24(7), 1977–1997. doi:10.1016/j.ymssp.2010.05.013
   Web of Science ®Google Scholar
• Kostic, B., & Gül, M. (2017). Vibration based damage detection of bridges under varying temperature effects using time series analysis and artificial neural networks. Journal of Bridge Engineering, 22(10), 04017065. doi:10.1061/(ASCE)BE.1943-5592.0001085
   Web of Science ®Google Scholar
• Lee, G. C., Mohan, S. B., Huang, C., & Fard, B. N. (2013). A study of U.S. bridge failures (1980–2012) (Technical report MCEER-13-0008). Federal Highway Administration, USA.
   Google Scholar
• Li, S. Z., & Wu, Z. S. (2007). Development of distributed long-gauge fiber optic sensing system for structural health monitoring. Structural Health Monitoring: An International Journal, 6(2), 133–143. doi:10.1177/1475921706072078
   Web of Science ®Google Scholar
• Li, Y. Y. (2010). Hypersensitivity of strain-based indicators for structural damage identification: A review. Mechanical Systems and Signal Processing, 24(3), 653–664. doi:10.1016/j.ymssp.2009.11.002
   Web of Science ®Google Scholar
• Lu, Z. R., & Liu, J. K. (2011). Identification of both structural damages in bridge deck and vehicular parameters using measured dynamic responses. Computers & Structures, 89(13–14), 1397–1405. doi:10.1016/j.compstruc.2011.03.008
   Web of Science ®Google Scholar
• Mehrjo, M., Khaji, N., Moharrami, H., & Bahreininejad, A. (2008). Damage detection of truss bridge joints using Artificial Neural Networks. Expert Systems with Applications, 35(3), 1122–1131. doi:10.1016/j.eswa.2007.08.008
   Web of Science ®Google Scholar
• Moaveni, B., Hurlebaus, S., & Moon, F. (2013). Special issue on real-world applications of structural identification and health monitoring methodologies. Journal of Structural Engineering, 139(10), 1637–1638. doi:10.1061/(ASCE)ST.1943-541X.0000779
   Web of Science ®Google Scholar
• Nuno, K. (2013). Damage detection of a steel truss bridge using frequency response function curvature method (ISRN KTH/BKN/R-148-SE, ISSN 1103-4289). Stockholm, Sweden.
   Google Scholar
• Office of Comptroller and Auditor General of India. (2015). Compliance audit on union government railways (Report no. 24 Part 2, India).
   Google Scholar
• Otter, D., Joy, R., Jones, M. C., & Maal, L. (2012). Need for bridge monitoring systems to counter railroad bridge service interruptions. Transportation Research Record: Journal of the Transportation Research Board, 2313(1), 134–143. doi:10.3141/2313-15
   Google Scholar
• Peeters, B., & Roeck, G. D. (2000). One year monitoring of the Z24-bridge: Environmental influences versus damage events. In Proceedings of IMAC 18, the international modal analysis conference, San Antonio, TX, USA (pp. 1570–1576).
   Google Scholar
• Posenato, D., Kripakaran, P., Inaudi, D., & Smith, I. F. C. (2010). Methodologies for model-free data interpretation of civil engineering structures. Computers & Structures, 88(7–8), 467–482. doi:10.1016/j.compstruc.2010.01.001
   Web of Science ®Google Scholar
• Prajapat, K., & Ray-Chaudhuri, S. (2017). Damage detection in railway truss bridges employing data sensitivity under Bayesian framework: A numerical investigation. Shock and Vibration, 2017, 1–9. doi:10.1155/2017/6423039
   Web of Science ®Google Scholar
• Quirke, P., Bowe, C., Obrien, E. J., Cantero, D., Antolin, P., & Goicolea, J. M. (2017). Railway bridge damage detection using vehicle-based inertial measurements and apparent profile. Engineering Structures, 153, 421–442. doi:10.1016/j.engstruct.2017.10.023
   Web of Science ®Google Scholar
• Rakoczy, A. M., Nowak, A. S., & Dick, S. (2016). Fatigue reliability model for steel railway bridges. Structure and Infrastructure Engineering, 12(12), 1602–1613. doi:10.1080/15732479.2016.1153664
   Web of Science ®Google Scholar
• Ralbovsky, M., Deix, S., & Flesch, R. (2010). Frequency changes in frequency-based damage identification. Structure and Infrastructure Engineering, 6(5), 611–619. doi:10.1080/15732470903068854
   Web of Science ®Google Scholar
• Sadhu, A., Narasimhan, S., & Goldack, A. (2014). Decentralized Modal Identification of a Pony Truss Pedestrian Bridge Using Wireless Sensors. Journal of Bridge Engineering, 19(6), 04014013 doi:10.1061/(ASCE)BE.1943-5592.0000552.
   Web of Science ®Google Scholar
• Salcher, P., Pradlwarter, H., & Adam, C. (2014). Reliability of high-speed railway bridges with respect to uncertain characteristics. Proceedings of the 9th International Conference on Structural Dynamics, Porto, Portugal.
   Google Scholar
• Siriwardane, S. C. (2015). Vibration measurement-based simple technique for damage detection of truss bridges: A case study. Case Studies in Engineering Failure Analysis, 4, 50–58. doi:10.1016/j.csefa.2015.08.001
   Google Scholar
• Sweeney, R. A. P., & Unsworth, J. F. (2010). Bridge inspection practice: Two different North American railways. Journal of Bridge Engineering, 15(4), 439–444. doi:10.1061/(ASCE)BE.1943-5592.0000001
   Web of Science ®Google Scholar
• Uppal, A. S. (2005). Coping with the older railroad steel bridges. Edmonton, AB: IMA Infrastructure Engineering Inc.
   Google Scholar
• Van Der Kooi, K., & Hoult, N. A. (2018). Assessment of a steel model truss using distributed fibre optic strain sensing. Engineering Structures, 171, 557–568. doi:10.1016/j.engstruct.2018.05.100
   Web of Science ®Google Scholar
• Vagnoli, M., Remenyte-Prescott, R., & Andrews, J. (2018). Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges. Structural Health Monitoring, 17(4), 971–1007. doi:10.1177/1475921717721137
   Web of Science ®Google Scholar
• Wang, F. L., Chan, T. H. T., Thambiratnam, D. P., Tan, A. C. C., & Cowled, C. J. L. (2012). Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms. Advances in Structural Engineering, 15(5), 693–706. doi:10.1260/1369-4332.15.5.693
   Web of Science ®Google Scholar
• Xu, Z.-D., Liu, M., Wu, Z., & Zeng, X. (2011). Energy damage detection strategy based on strain responses for long-span bridge structures. Journal of Bridge Engineering, 16 (5), 644–652. doi:10.1061/(ASCE)BE.1943-5592.0000195
   Web of Science ®Google Scholar
• Yam, L.Y., Leung, T.P., Li, D.B., & Xue, K.Z. (1996). Theoretical and experimental study of modal strain analysis. Journal of Sound and Vibration, 191(2), 251–260. doi:10.1006/jsvi.1996.0119.
   Web of Science ®Google Scholar
• Zhan, J. W., Xia, H., Chen, S. Y., & Roeck, G. D. (2011). Structural damage identification for railway bridges based on train-induced bridge responses and sensitivity analysis. Journal of Sound and Vibration, 330(4), 757–770. doi:10.1016/j.jsv.2010.08.031
   Web of Science ®Google Scholar
• Zhang, H., Gül, M., & Kostic, B. (2019). Eliminating temperature effects in damage detection for civil infrastructures using times series analysis and auto-associative neural networks. Journal of Aerospace Engineering, 32(2), 04019001. doi:10.1061/(ASCE)AS.1943-5525.0000987
   Web of Science ®Google Scholar