Structural Monitoring and Maintenance

  • 제6권2호
  • /
  • Pages.125-145
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  • 2019
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  • 2288-6605(pISSN)
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  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Vibration based bridge scour evaluation: A data-driven method using support vector machines

  • Zhang, Zhiming (Department of Civil and Environmental Engineering, Louisiana State University) ;
  • Sun, Chao (Department of Civil and Environmental Engineering, Louisiana State University) ;
  • Li, Changbin (Department of Computer Science, University of Texas at Dallas) ;
  • Sun, Mingxuan (Division of Computer Science and Engineering, Louisiana State University)
  • 투고 : 2019.02.28
  • 심사 : 2019.04.18
  • 발행 : 2019.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

키워드

과제정보

연구 과제 주관 기관 : Louisiana State University

참고문헌

  1. Adams, R.D., Cawley, P., Pye, C.J. and Stone, B.J. (1978), "A vibration technique for non-destructively assessing the integrity of structures", J. Mech. Eng. Sci., 20(2), 93-100. https://doi.org/10.1243/JMES_JOUR_1978_020_016_02
  2. Allemang, R.J. and Brown, D.L. (1982), "A correlation coefficient for modal vector analysis", Proceedings of the 1st international modal analysis conference, Orlando: Union College Press.
  3. Azhari, F., Scheel, P.J. and Loh, K.J. (2015), "Monitoring bridge scour using dissolved oxygen probes", Struct. Monit. Maint., 2(2), 145-164. https://doi.org/10.12989/SMM.2015.2.2.145
  4. Bao, T., Swartz, R.A., Vitton, S., Sun, Y., Zhang, C. and Liu, Z. (2017), "Critical insights for advanced bridge scour detection using the natural frequency", J. Sound Vib., 386, 116-133. https://doi.org/10.1016/j.jsv.2016.06.039
  5. Barthorpe, R.J., Manson, G. and Worden, K. (2017), "On multi-site damage identification using single-site training data", J. Sound Vib., 409, 43-64. https://doi.org/10.1016/j.jsv.2017.07.038
  6. Bishop, C.M. (2016), Pattern Recognition and Machine Learning. Springer-Verlag New York.
  7. Bolduc, L.C., Gardoni, P. and Briaud, J.L. (2008), "Probability of exceedance estimates for scour depth around bridge piers", J. Geotech. Geoenviron. Eng., 134(2), 175-184. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:2(175)
  8. Briaud, J.L., Hurlebaus, S., Chang, K.A., Yao, C., Sharma, H., Yu, O.Y., Darby, C., Hunt, B.E. and Price, G.R. (2011), "Realtime monitoring of bridge scour using remote monitoring technology", Austin, TX.
  9. Briaud, J.L., Yao, C., Darby, C., Sharma, H., Hurlebaus, S., Price, G.R., Chang, K.A., Hunt, B.E. and Yu. O.Y. (2010), "Motion sensors for scour monitoring: laboratory experiments and numerical simulations", Transportation Research Board (TRB) 89th Annual Meeting, 10-14.
  10. Casas, J.R. and Aparicio, A.C. (1994), "Structural damage identification from dynamic-test data", J. Struct. Eng., 120(8), 2437-2450. https://doi.org/10.1061/(ASCE)0733-9445(1994)120:8(2437)
  11. Cawley, P. and Adams, R.D. (1979), "The location of defects in structures from measurements of natural frequencies", J. Strain Anal. Eng. Des., 14(2), 49-57. https://doi.org/10.1243/03093247V142049
  12. Chen, Y., Joffre, D. and Avitabile, P. (2018), "Underwater dynamic response at limited points expanded to full-field strain response", J. Vib. Acoust., 140(5), 051016. https://doi.org/10.1115/1.4039800
  13. Chen, Y., Logan, P., Avitabile, P. and Dodson, J. (2019), "Non-model based expansion from limited points to an augmented set of points using Chebyshev polynomials", Exp. Techniques, 1-23.
  14. Cortes, C. and Cortes, V. (1995), "Support-vector networks", Machine learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018
  15. David Martin Powers (2011), Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation.
  16. Deng, L. and Cai, C.S. (2009), "Bridge scour: Prediction, modeling, monitoring, and countermeasures", Practice periodical on structural design and construction, 15(2), 125-134. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000041
  17. Doebling, S.W., Farrar, C.R., Prime, M.B. and Shevitz, D.W. (1996), Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review.
  18. Elsaid, A. (2012), "Vibration based damage detection of scour in coastal bridges", PhD thesis, North Carolina State University.
  19. Elsaid, A. and Seracino, R. (2014), "Rapid assessment of foundation scour using the dynamic features of bridge superstructure", Constr. Build. Mater., 50, 42-49. https://doi.org/10.1016/j.conbuildmat.2013.08.079
  20. Farrar, C.R. and James III, G.H. (1997), "System identification from ambient vibration measurements on a bridge", J. Sound Vib., 205(1), 1-18. https://doi.org/10.1006/jsvi.1997.0977
  21. Farrar, C.R. and Worden, K. (2012), Structural health monitoring: a machine learning perspective, John Wiley & Sons.
  22. Foti, S. and Sabia, D. (2010), "Influence of foundation scour on the dynamic response of an existing bridge", J. Bridge Eng., 16(2), 295-304. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000146
  23. He, X., Cai, D. and Niyogi, P. (2006), "Laplacian score for feature selection", Adv. Neural Inform. Process. Syst., 507-514.
  24. Huang, C. and Nagarajaiah, S. "Experimental study on bridge structural health monitoring using blind source separation method: arch bridge", Struct. Monit. Maint., 1(1), 69-87. https://doi.org/10.12989/smm.2014.1.1.069
  25. Ju, S.H. (2013), "Determination of scoured bridge natural frequencies with soil-structure interaction", Soil Dynam. Earthq.Eng., 55, 247-254. https://doi.org/10.1016/j.soildyn.2013.09.015
  26. Kaminski, P.C. (1995), "The approximate location of damage through the analysis of natural frequencies with artificial neural networks", Proceedings of the Institution of Mechanical Engineers, Part E: J. Process Mech. Eng., 209(2), 117-123. https://doi.org/10.1243/PIME_PROC_1995_209_238_02
  27. Kim, J.H., Jeon, H.S. and Lee, C.W. (1992), "Applications of the modal assurance criteria for detecting and locating structural faults", Proceedings of the International Modal Analysis Conference, pages 536-536. SEM Society for Experimental Mechanics Inc.
  28. Kim, J.T., Ryu, Y.S., Cho, H.M. and Stubbs, N. (2003), "Damage identification in beamtype structures: frequency-based method vs mode-shape-based method", Eng. Struct., 25(1), 57-67. https://doi.org/10.1016/S0141-0296(02)00118-9
  29. Kong, S. and Cai, S.C. (2016), "Scour effect on bridge and vehicle responses under bridge-vehicle-wave interaction", J. Bridge Eng., 21(4), 04015083. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000868
  30. Lai, C., Reinders, M.J.T. and Wessels, L. (2006), "Random subspace method for multivariate feature selection", Pattern Recogn. Lett., 27(10), 1067-1076. https://doi.org/10.1016/j.patrec.2005.12.018
  31. Lam, H.F., Ko, J.M. and Wong, C.W. (1998), "Localization of damaged structural connections based on experimental modal and sensitivity analysis", J. Sound Vib., 210(1), 91-115. https://doi.org/10.1006/jsvi.1997.1302
  32. Lee, Y.S. and Chung, M.J. (2000), "A study on crack detection using eigenfrequency test data", Comput. Struct., 77(3), 327-342. https://doi.org/10.1016/S0045-7949(99)00194-7
  33. Li, L., Liu, G., Zhang, L. and Li, Q. (2018), "Deep learning-based sensor fault detection using s-long short term memory networks", Struct. Monit. Maint., 5(1), 51-65. https://doi.org/10.12989/SMM.2018.5.1.051
  34. Li, Z. and Burgue, R. (2010), "Using soft computing to analyze inspection results for bridge evaluation and management", J. Bridge Eng., 15(4), 430-438. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000072
  35. Lv, Y., Liu, Z. and Li, Z. (2015), "Seismic response analysis for unequal height pier bridges considering soil-pile interaction", J. Vib. Shock, 35(23), 114.
  36. May, R.W.P., Ackers, J.C. and Kirby, A.M. (2002), Manual on scour at bridges and other hydraulic structures, 551. Ciria London.
  37. Melville, B.W. and Coleman, S.E. (2000), Bridge scour, Water Resources Publication.
  38. Nagarajaiah, S. and Chen, B. (2016), "Output only structural modal identification using matrix pencil method", Struct. Monit. Maint., 3(4), 395-406. https://doi.org/10.12989/smm.2016.3.4.395
  39. Ng, A.Y. (2004), "Feature selection, l 1 vs. l 2 regularization, and rotational invariance", Proceedings of the 21st international conference on Machine learning, ACM.
  40. Ni, Y.Q., Hua, X.G., Fan, K.Q. and Ko, J.M. (2005), "Correlating modal properties with temperature using long-term monitoring data and support vector machine technique", Eng. Struct., 27(12), 1762-1773. https://doi.org/10.1016/j.engstruct.2005.02.020
  41. Oh, C.K. and Sohn. H. (2008), "Damage diagnosis under environmental and operational variations using unsupervised support vector machine", J. Sound Vib., 325(1-2), 224-239. https://doi.org/10.1016/j.jsv.2009.03.014
  42. Pandey, A.K., Biswas, M. and Samman, M.M. (1991), "Damage detection from changes in curvature mode shapes", J. Sound Vib., 145(2), 321-332. https://doi.org/10.1016/0022-460X(91)90595-B
  43. Prendergast, L.J., Hester, D. and Gavin, K. (2016), "Determining the presence of scour around bridge foundations using vehicle-induced vibrations", J. Bridge Eng., 21(10), 04016065. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000931
  44. Prendergast, L.J., Hester, D., Gavin, K. and OSullivan, J.J. (2013), "An investigation of the changes in the natural frequency of a pile affected by scour", J. Sound Vib., 332(25), 6685-6702. https://doi.org/10.1016/j.jsv.2013.08.020
  45. Qin, Y., Dong, M., Zhao, F., Langari, R. and Gu, L. (2015), "Road profile classification for vehicle semi-active suspension system based on adaptive neuro-fuzzy inference system", Proceedings of the Decision and Control (CDC), 2015 IEEE 54th Annual Conference on.
  46. Rytter., A. (1993), Vibrational based inspection of civil engineering structures, PhD thesis, Aalborg University.
  47. Saha, B., Goebel, K., Poll, S. and Christophersen, J. (2009), "Prognostics methods for battery health monitoring using a bayesian framework", IEEE T. Instrum. Meas., 58(2), 291-296. https://doi.org/10.1109/TIM.2008.2005965
  48. Satish Nagarajaiah and Kalil Erazo. (2016), "Structural monitoring and identification of civil infrastructure in the United States", Struct. Monit. Maint., 3(1), 51-69. https://doi.org/10.12989/smm.2016.3.1.051
  49. Sen, D., AAghazadeh, A., Mousavi, A., Nagarajaiah, S. and Baraniuk, R. (2019), "Sparsitybased approaches for damage detection in plates", Mech. Syst. Signal Pr., 117, 333-346. https://doi.org/10.1016/j.ymssp.2018.08.019
  50. Sen, D., Nagarajaiah, S. and Gopalakrishnan, S. (2017), "Harnessing sparsity in lamb wave-based damage detection for beams", Struct. Monit. Maint., 4(4), 381-396. https://doi.org/10.12989/SMM.2017.4.4.381
  51. Shirole, A.M. and Holt, R.C. (1991), "Planning for a comprehensive bridge safety assurance program", Transportation Research Record, 1290, 39-50.
  52. Shirole, A.M. and Holt, R.C. (2009), Monitoring Scour Critical Bridges-A NCHRP, Synthesis of highway, practice,Washington, DC.
  53. Wahab, M.M.A. and Roeck, G.D. (1999), "Damage detection in bridges using modal curvatures: application to a real damage scenario", J. Sound Vib., 226(2), 217-235. https://doi.org/10.1006/jsvi.1999.2295
  54. Worden, K. and Lane, A.J. (2001), "Damage identification using support vector machines", Smart Mater. Struct., 10(3), 540. https://doi.org/10.1088/0964-1726/10/3/317
  55. Yang, Y. and Nagarajaiah, S. (2012), "Time-frequency blind source separation using independent component analysis for output-only modal identification of highly damped structures", J. Struct. Eng., 139(10), 1780-1793. https://doi.org/10.1061/(asce)st.1943-541x.0000621
  56. Yao, R. and Pakzad, S.N. (2012), "Autoregressive statistical pattern recognition algorithms for damage detection in civil structures", Mech. Syst. Signal Pr., 31, 355-368. https://doi.org/10.1016/j.ymssp.2012.02.014
  57. Yu, H., Zhu, H.P., Weng, S., Gao, F., Luo, H. and Ai, De M. (2018), "Damage detection of subway tunnel lining through statistical pattern recognition", Struct. Monit. Maint., 5(2), 231-242. https://doi.org/10.12989/SMM.2018.5.2.231
  58. Zhao, J. and Zhang, L. (2012), "Structural damage identification based on the modal data change", Int. J. Eng. Manufact. (IJEM), 2(4), 59. https://doi.org/10.5815/ijem.2012.04.08

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