Identification of damage parameters during flood events applicable to multi-span bridges

Abstract

During flood events, the dynamic interaction between flowing water and bridges generates random loadings that force bridges to vibrate in all six degrees of freedom. It is difficult for a structural damage detection method to select a degree of freedom, or damage feature, to accurately describe and predict damage. The methodology presented here identifies damage-sensitive features and uses them to monitor bridge health. A small-scale physical model of a multi-span highway bridge was constructed to satisfy geometrical, Cauchy, and Froude similarities, and six-dimensional hydrodynamic forces induced by simulated flood events were investigated as an input excitation in a tilting flume. It was determined that pitch, roll, and surge motions can be used as damage features during the inundated stage, while pitch, roll, surge, and heave can be used before the inundated stage. In addition, angular velocity signals exhibited more consistent damage indices than acceleration. Using the damage features, the proposed algorithm could successfully detect damage and damage severity during simulated flood stages. Identifying damage features can reduce the size of the collected data and inform emergency responders’ decisions. This case study can be used to test methods at full scale on similar structures to develop automated health-monitoring systems.

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References

  1. 1.

    American Society of Civil Engineers (2017) Infrastructure report card. https://www.infrastructurereportcard.org. Accessed 20 Mar 2020

  2. 2.

    Wardhana K, Hadipriono FC (2003) Analysis of recent bridge failures in the United States. J Perform Constr Facil 17(3):144–150. https://doi.org/10.1061/(ASCE)0887-3828(2003)17:3(144)

    Article  Google Scholar 

  3. 3.

    Chen X, Zhan J, Chen Q, Cox D (2016) Numerical modeling of wave forces on movable bridge decks. J Bridge Eng. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000922

    Article  Google Scholar 

  4. 4.

    Huang B, Zhu B, Cui S, Duan L, Zhang J (2018) Experimental and numerical modelling of wave forces on coastal bridge superstructures with box girders, Part I: Regular waves. Ocean Eng 149:53–77. https://doi.org/10.1016/j.oceaneng.2017.11.046

    Article  Google Scholar 

  5. 5.

    Azadbakht M, Yim S (2015) Simulation and estimation of tsunami loads on bridge superstructures. J Waterw Port Coast. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000262

    Article  Google Scholar 

  6. 6.

    Guo A, Fang Q, Li H (2015) Analytical solution of hurricane wave forces acting on submerged bridge decks. Ocean Eng 108:519–528. https://doi.org/10.1016/j.oceaneng.2015.08.018

    Article  Google Scholar 

  7. 7.

    Xu G, Chen Q, Chen J (2018) Prediction of solitary wave forces on coastal bridge decks using artificial neural networks. J Bridge Eng. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001215

    Article  Google Scholar 

  8. 8.

    Chen Q, Wang L, Zhao H (2009) Hydrodynamic investigation of coastal bridge collapse during Hurricane Katrina. J Hydraul Eng 135(3):175–186. https://doi.org/10.1061/(ASCE)0733-9429(2009)135:3(175)

    Article  Google Scholar 

  9. 9.

    Prendergast LJ, Limongelli MP, Ademovic N, Anžlin A, Gavin K, Zanini M (2018) Structural health monitoring for performance assessment of bridges under flooding and seismic actions. Struct Eng Int 28(3):296–307. https://doi.org/10.1080/10168664.2018.1472534

    Article  Google Scholar 

  10. 10.

    Lau T, Ohmachi T, Inoue S, Lukkunaprasit P (2011) Experimental and numerical modeling of tsunami force on bridge decks [open access peer-reviewed chapter]. Intech, London. https://doi.org/10.5772/23622

    Book  Google Scholar 

  11. 11.

    Ryan TW, Mann JE, Chill ZM, Ott BT (2012) Bridge inspector’s reference manual (BIRM) (Publication No. FHWA NHI 12-049). Federal Highway Administration, Washington, DC

    Google Scholar 

  12. 12.

    Schallhorn C, Rahmatalla S (2015) Crack detection and health monitoring of highway steel-girder bridges. Struct Health Monit 14(3):281–299. https://doi.org/10.1177/1475921714568404

    Article  Google Scholar 

  13. 13.

    Schallhorn C, Rahmatalla S (2015) Damage detection of retrofitted crack re-initiation and growth. J Civil Struct Health Monit 5:377. https://doi.org/10.1007/s13349-015-0113-z

    Article  Google Scholar 

  14. 14.

    Harms T, Bastianini F, Sedigh Sarvestani S (2008) An embedded wireless system for remote monitoring of bridges. In: Proceedings volume 6932, Sensors and smart structures technologies for civil, mechanical, and aerospace systems, p 693217. https://doi.org/10.1117/12.780421

  15. 15.

    Selvakumaran S, Plank S, Geiß C, Rossi C, Middleton C (2018) Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques. Int J Appl Earth Obs Geoinf 73:463–470. https://doi.org/10.1016/j.jag.2018.07.004

    Article  Google Scholar 

  16. 16.

    Sousa JJ, Hlaváčová I, Bakoň M, Lazecký M, Patrício G, Guimarães P, Ruiz AM, Bastos L, Sousa A, Bento R (2014) Potential of multi-temporal InSAR techniques for bridges and dams monitoring. Proced Technol 16:834–841. https://doi.org/10.1016/j.protcy.2014.10.033

    Article  Google Scholar 

  17. 17.

    Bao T, Liu Z (2017) Vibration-based bridge scour detection: a review. Struct Control Health 24:e1937. https://doi.org/10.1002/stc.1937

    Article  Google Scholar 

  18. 18.

    Dunbar D, Bayik B, Omenzetter P, Van der Dominic A (2018) Experimental ambient vibration-based structural health monitoring in top-tensioned risers. In: Proceedings volume 10601, Smart materials and nondestructive evaluation for energy systems IV, p 1060107. https://doi.org/10.1117/12.2294492

  19. 19.

    Rahmatalla S, Hudson K, Liu Y, Eun H (2014) Finite element modal analysis and vibration-waveforms in health inspection of old bridges. Finite Elem Anal Des 78:40–46. https://doi.org/10.1016/j.finel.2013.09.006

    Article  Google Scholar 

  20. 20.

    Elsaid A, 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

    Article  Google Scholar 

  21. 21.

    Mind'je R, Li L, Amanambu AC, Nahayo L, Nsengiyumva JB, Gasirabo A, Mindje M (2019) Flood susceptibility modeling and hazard perception in Rwanda. Int J Disaster Risk Reduct. https://doi.org/10.1016/j.ijdrr.2019.101211

    Article  Google Scholar 

  22. 22.

    Kalantari Z, Ferreira CSS, Koutsouris AJ, Ahlmer AK, Cerdà A, Destouni G (2019) Assessing flood probability for transportation infrastructure based on catchment characteristics, sediment connectivity and remotely sensed soil moisture. Sci Total Environ 661:393–406. https://doi.org/10.1016/j.scitotenv.2019.01.009

    Article  Google Scholar 

  23. 23.

    Barankin RA, Kirshen P, Watson C, Douglas E, DiNezio S, Miller S, Bosma KF, McArthur K, Bowen RE (2020) Hierarchical approach for assessing the vulnerability of roads and bridges to flooding in Massachusetts. J Infrastruct Syst 26(3):04020028. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000564

    Article  Google Scholar 

  24. 24.

    Argyroudis SA, Mitoulis SA, Hofer L, Zanini MA, Tubaldi E, Frangopol DM (2020) Resilience assessment framework for critical infrastructure in a multi-hazard environment: case study on transport assets. Sci Total Environ 714:136854

    Article  Google Scholar 

  25. 25.

    Lamb R, Garside P, Pant R, Hall JW (2019) A probabilistic model of the economic risk to Britain's railway network from bridge scour during floods. Risk Anal 39(11):2457–2478. https://doi.org/10.1111/risa.13370

    Article  Google Scholar 

  26. 26.

    Prendergast LJ, Gavin K (2014) A review of bridge scour monitoring techniques. J Rock Mech Geotech Eng 6:138–149. https://doi.org/10.1016/j.jrmge.2014.01.007

    Article  Google Scholar 

  27. 27.

    Briaud JL, Hurlebaus S, Chang KA, Yao C, Sharma H, Yu OY, Darby C, Hunt BE, Price GR, (2011) Realtime monitoring of bridge scour using remote monitoring technology (Report no. 0-6060-1). Texas Transportation Institute, College Station, TX

  28. 28.

    Tang F, Chen Y, Li Z, Hu X, Chen G, Tang Y (2019) Characterization and field validation of smart rocks for bridge scour monitoring. Struct Health Monit 18(5–6):1669–1685. https://doi.org/10.1177/1475921718824944

    Article  Google Scholar 

  29. 29.

    Akay H, Baduna Kocyigit M, Yanmaz AM (2019) Development of a safety-inspection method for river bridges in Turkey. Water 11(9):1902. https://doi.org/10.3390/w11091902

    Article  Google Scholar 

  30. 30.

    Robertson IN, Riggs HR, Yim SCS, Young YL (2007) Lessons from Hurricane Katrina storm surge on bridges and buildings. J Waterw Port Coast 133(6):463–483. https://doi.org/10.1061/(ASCE)0733-950X(2007)133:6(463)

    Article  Google Scholar 

  31. 31.

    Al-Jailawi S, Rahmatalla S (2017) Damage detection in structures using angular velocity. J Civ Struct Health Monit 7:359. https://doi.org/10.1007/s13349-017-0224-9

    Article  Google Scholar 

  32. 32.

    Al-Jailawi S, Rahmatalla S (2018) Transmissibility-based damage detection using angular velocity versus acceleration. J Civ Struct Health Monit 8:649. https://doi.org/10.1007/s13349-018-0297-0

    Article  Google Scholar 

  33. 33.

    Jin J, Meng B (2011) Computation of wave loads on the superstructures of coastal highway bridges. Ocean Eng 38:2185–2200. https://doi.org/10.1016/j.oceaneng.2011.09.029

    Article  Google Scholar 

  34. 34.

    Harris H, Gajanam MS (1999) Structural modeling and experimental techniques, 2nd edn. CRC Press, Boca Raton

    Book  Google Scholar 

  35. 35.

    Gibbings JC (2011) Dimensional analysis. Springer, London

    Book  Google Scholar 

  36. 36.

    Ramu M, Prabhu Raja V, Thyla PR (2013) Establishment of structural similitude for elastic models and validation of scaling laws. KSCE J Civ Eng 17(1):139–144. https://doi.org/10.1007/s12205-013-1216-x

    Article  Google Scholar 

  37. 37.

    Wei C, Zhou D, Ou J (2019) Wave and wave-current actions on a bridge tower: an experimental study. Adv Struct Eng 22(6):1467–1478

    Article  Google Scholar 

  38. 38.

    Henry AM (2011) Wave forces on bridge decks and damping techniques to reduce damages. Louisiana State University, Baton Rouge

    Google Scholar 

  39. 39.

    Xu G, Cai CS (2017) Numerical investigation of the lateral restraining stiffness effect on the bridge deck-wave interaction under Stokes waves. Eng Struct 130:112–123

    Article  Google Scholar 

  40. 40.

    Cozijn H, Serraris JJ (2017) Hydrodynamic scale model tests for offshore structures. Encycl Marit Offshore Eng. https://doi.org/10.1002/9781118476406.emoe329

    Article  Google Scholar 

  41. 41.

    Sheppard DM, Marin J (2009) Wave loading on bridge decks (final report submitted to Florida Department of Transportation). https://rosap.ntl.bts.gov/view/dot/17467/dot/17467

  42. 42.

    Wei C, Zhou D, Ou J (2017) Experimental study of the hydrodynamic responses of a bridge tower to waves and wave currents. J Waterw Port Coast Ocean Eng 143(3):04017002. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000381

    Article  Google Scholar 

  43. 43.

    Young YL (2010) Dynamic hydroelastic scaling of self-adaptive composite marine rotors. Compos Struct 92(1):97–106. https://doi.org/10.1016/j.compstruct.2009.07.001

    Article  Google Scholar 

  44. 44.

    Heller V (2011) Scale effects in physical hydraulic engineering models. J Hydraul Res 49(3):293–306. https://doi.org/10.1080/00221686.2011.578914

    Article  Google Scholar 

  45. 45.

    Shumin C, Swamidas ASJ, Sharp JJ (1996) Similarity method for modelling hydroelastic offshore platforms. Ocean Eng 23(7):575–595. https://doi.org/10.1016/0029-8018(95)00050-X

    Article  Google Scholar 

  46. 46.

    Ciappi E, Magionesi F, De Rosa S, Franco F (2009) Hydrodynamic and hydroelastic analyses of a plate excited by the turbulent boundary layer. J Fluids Struct 25(2):321–342. https://doi.org/10.1016/j.jfluidstructs.2008.04.006

    Article  Google Scholar 

  47. 47.

    United State Geological Survey (2020) USGS 05464730 Cedar River below Indian Creek at Cedar Rapids, IA. https://waterdata.usgs.gov/nwis/uv?05464730. Accessed 20 Mar 2020

  48. 48.

    National Weather Service (2020) Hydrograph: Cedar River at Cedar Rapids. https://water.weather.gov/ahps2/hydrograph.php?wfo=dvn&gage=cidi4. Accessed 20 Mar 2020

  49. 49.

    Park YS, Kim S, Kim N, Lee JJ (2017) Finite element model updating considering boundary conditions using neural networks. Eng Struct 150:511–519. https://doi.org/10.1016/j.engstruct.2017.07.032

    Article  Google Scholar 

  50. 50.

    Park YS, Kim S, Kim N, Lee JJ (2019) Evaluation of bridge support condition using bridge responses. Struct Health Monit 18(3):767–777. https://doi.org/10.1177/1475921718773672

    Article  Google Scholar 

  51. 51.

    Brandt A (2011) Noise and vibration analysis: signal analysis and experimental procedures. Wiley, Hoboken

    Book  Google Scholar 

  52. 52.

    Brincker R, Ventura C (2015) Introduction to operational modal analysis. Wiley, Hoboken

    Book  Google Scholar 

  53. 53.

    Avitabile P (2018) Modal testing: a practitioner’s guide. Wiley, Hoboken

    Google Scholar 

Download references

Funding

The research described in this paper is funded by the Mid-America Transportation Center via a grant from the US Department of Transportation’s University Transportation Centers Program (Grant number: DOT 69A3551747107), and this support is gratefully acknowledged. The contents reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein and are not necessarily representative of the sponsoring agencies.

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Correspondence to Salam Rahmatalla.

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Karimpour, A., Rahmatalla, S. & Markfort, C. Identification of damage parameters during flood events applicable to multi-span bridges. J Civil Struct Health Monit 10, 973–985 (2020). https://doi.org/10.1007/s13349-020-00429-w

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Keywords

  • Structural health monitoring
  • Damage index
  • Flooding
  • Hydrodynamic loading
  • Angular velocity