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Application of artificial neural network to predict dynamic displacements from measured strains for a highway bridge under traffic loads

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Abstract

This paper addresses the experimental verification of vertical displacement predictions for a highway bridge under dynamic vehicle loads. In the structural health monitoring of bridge structures, the measurements of vertical displacements are relatively difficult than the measurements of axial strains, and the vertical displacements can give an intuitive information for monitoring the structural conditions. Therefore, an artificial neural network (ANN) was introduced for the accurate predictions of the vertical displacements from the axial strains. In the experiments, both the strains and displacements at the different locations were measured during 48 h for obtaining the training (Set 1) and testing (Set 2) data, which were utilized to develop and validate the ANN-based prediction model. The environmental effects such as temperature loads were eliminated from the experimental data using a calibration approach, and the pure contributions of the external vehicle loadings to the 3D highway bridge were considered. The validation results showed that the ANN-based model can accurately predict the vertical displacements. It is expected that the proposed ANN-based model, as a fast and accurate framework, can be utilized for various civil infrastructures during the structural health monitoring.

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References

  1. Ribeiro D, Calçada R, Ferreira J, Martins T (2014) Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system. Eng Struct 75:164–180

    Article  Google Scholar 

  2. Lee JJ, Cho S, Shinozuka M, Yun C-B, Lee C-G, Lee W-T (2006) Evaluation of bridge load carrying capacity based on dynamic displacement measurement using real-time image processing techniques. Steel Struct 6:377–385

    Google Scholar 

  3. Lee JJ, Shinozuka M (2006) A vision-based system for remote sensing of bridge displacement. NDT E Int 39:425–431

    Article  Google Scholar 

  4. Lee JJ, Fukuda Y, Shinozuka M, Cho S, Yun C-B (2007) Development and application of a vision-based displacement measurement system for structural health monitoring of civil structures. Smart Struct Syst 3:373–384

    Article  Google Scholar 

  5. Ji Y, Zhang O (2012) A novel image-based approach for structural displacement measurement. In: Proceedings of 6th International Conference on Bridge Maintenance, Safety Manage, pp 407–414

  6. Yu J, Zhu P, Xu B, Meng X (2017) Experimental assessment of high sampling-rate robotic total station for monitoring bridge dynamic responses. Measurement 104:60–69

    Article  Google Scholar 

  7. Wahbeh AM, Caffrey JP, Masri SF (2003) A vision-based approach for the direct measurement of displacements in vibrating systems. Smart Mater Struct 12:785–794

    Article  Google Scholar 

  8. Kim NS, Cho NS (2004) Estimating deflection of a simple beam model using fiber optic Bragg-grating sensors. Exp Mech 44:433–439

    Article  Google Scholar 

  9. Park K-T, Kim S-H, Park H-S, Lee K-W (2005) The determination of bridge displacement using measured acceleration. Eng Struct 27:371–378

    Article  Google Scholar 

  10. Wang Z-C, Geng D, Ren W-X, Liu H-T (2014) Strain modes based dynamic displacement estimation of beam structures with strain sensors. Smart Mater Struct 23:125045

    Article  Google Scholar 

  11. Arias-Lara D, De-la-Colina J (2018) Assessment of methodologies to estimate displacements from measured acceleration records. Measurement 114:261–273

    Article  Google Scholar 

  12. Kim K, Choi J, Chung J, Koo G, Bae I-H, Sohn H (2018) Structural displacement estimation through multi-rate fusion of accelerometer and RTK-GPS displacement and velocity measurements. Measurement 130:223–235

    Article  Google Scholar 

  13. Guan S, Rice JA, Li C, Li Y, Wang G (2017) Structural displacement measurements using DC coupled radar with active transponder. Struct Control Health Monit 24:e1909

    Article  Google Scholar 

  14. Chung W, Kim S, Kim N-S, Lee H-U (2008) Deflection estimation of a full scale prestressed concrete girder using long-gauge fiber optic sensors. Constr Build Mater 22:394–401

    Article  Google Scholar 

  15. Xu Y, Brownjohn J, Kong D (2018) A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge. Struct Control Health Monit 25:e2155

    Article  Google Scholar 

  16. Brown N, Schumacher T, Vicente MA (2021) Evaluation of a novel video-and laser-based displacement sensor prototype for civil infrastructure applications. J Civ Struct Heal Monit 11(2):265–281

    Article  Google Scholar 

  17. Lee J, Lee KC, Lee S, Lee YJ, Sim SH (2019) Long-term displacement measurement of bridges using a LiDAR system. Struct Control Health Monit 26:e2428

    Article  Google Scholar 

  18. Gindy M, Vaccaro R, Nassif H, Velde J (2008) A state-space approach for deriving bridge displacement from acceleration. Comput-Aided Civil Infrastruct Eng 23:281–290

    Article  Google Scholar 

  19. Nassif HH, Gindy M, Davis J (2005) Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration. NDT E Int 38:213–218

    Article  Google Scholar 

  20. Gentile C, Bernardini G (2010) An interferometric radar for non-contact measurement of deflections on civil engineering structures: laboratory and full-scale tests. Struct Infrastruct Eng 6:521–534

    Article  Google Scholar 

  21. Xu L, Guo JJ, Jiang JJ (2002) Time-frequency analysis of a suspension bridge based on Gps. J Sound Vib 254:105–116

    Article  Google Scholar 

  22. Yi T-H, Li H-N, Gu M (2013) Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge. Measurement 46:420–432

    Article  Google Scholar 

  23. Gaxiola-Camacho JR, Bennett R, Guzman-Acevedo GM, Gaxiola-Camacho IE (2017) Structural evaluation of dynamic and semi-static displacements of the Juarez Bridge using GPS technology. Measurement 110:146–153

    Article  Google Scholar 

  24. Xi R, Jiang W, Meng X, Chen H, Chen Q (2018) Bridge monitoring using BDS-RTK and GPS-RTK techniques. Measurement 120:128–139

    Article  Google Scholar 

  25. Kang L-H, Kim D-K, Han J-H (2007) Estimation of dynamic structural displacements using fiber Bragg grating strain sensors. J Sound Vib 305:534–542

    Article  Google Scholar 

  26. Glaser R, Caccese V, Shahinpoor M (2011) Shape monitoring of a beam structure from measured strain or curvature. Exp Mech 52:591–606

    Article  Google Scholar 

  27. Zheng W, Dan D, Cheng W, Xia Y (2019) Real-time dynamic displacement monitoring with double integration of acceleration based on recursive least squares method. Measurement 141:460–471

    Article  Google Scholar 

  28. Shrestha A, Dang J, Nakajima K, Wang X (2020) Image processing–based real-time displacement monitoring methods using smart devices. Struct Control Health Monit 27:e2473

    Article  Google Scholar 

  29. Jeon JC, Lee HH (2019) Development of displacement estimation method of girder bridges using measured strain signal induced by vehicular loads. Eng Struct 186:203–215

    Article  Google Scholar 

  30. Park JW, Sim SS, Jung HJ (2013) Displacement estimation using multimetric data fusion. IEEE/ASME Trans Mechatron 18(6):1675–1682

    Article  Google Scholar 

  31. Cho SJ, Yun CB, Sim SH (2015) Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model. Smart Struct Syst 15(3):645–663

    Article  Google Scholar 

  32. Moon HS, Ok S, Chun PJ, Lim YM (2019) Artificial neural network for vertical displacement prediction of a bridge from strains (part 1): girder bridge under moving vehicles. Appl Sci 9:2881

    Article  Google Scholar 

  33. Moon HS, Chun P-J, Kim MK, Lim YM (2020) Artificial neural network for vertical displacement prediction of a bridge from strains (part 2): optimization of strain-measurement points by a genetic algorithm under dynamic loading. Appl Sci 10:777

    Article  Google Scholar 

  34. Zurada JM (1992) Introduction to artificial neural systems. West St. Paul

  35. Fausett L (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall Inc, New York

    MATH  Google Scholar 

  36. Erzin Y, Rao BH, Singh DJIJOTS (2008) Artificial neural network models for predicting soil thermal resistivity. Int J Therm Sci 47(2008):1347–1358

    Article  Google Scholar 

  37. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  38. Rumelhart D, McClelland J, Group PR (1986) Learning internal representations by error propagation. Pararell Distributed processing: Explorations in the microestructure of cognition, 1–3. MIT Press, New York

    Google Scholar 

  39. Najjar YM, Basheer IA, Mcreynolds R (1996) Neural modeling of Kansas soil swelling. Transp Res Rec 1526:14–19

    Article  Google Scholar 

  40. Haque ME, Sudhakar K (2002) ANN back-propagation prediction model for fracture toughness in microalloy steel. Int J Fatigue 24:1003–1010

    Article  Google Scholar 

  41. Kim H, Rauch AF, Haas CT (2004) Automated quality assessment of stone aggregates based on laser imaging and a neural network. J Comput Civ Eng 18:58–64

    Article  Google Scholar 

  42. Singh T, Gupta A, Sain R (2006) A comparative analysis of cognitive systems for the prediction of drillability of rocks and wear factor, Geotechnical Geological. Engineering 24:299–312

    Google Scholar 

  43. Bai J, Wild S, Ware J, Sabir B (2003) Using neural networks to predict workability of concrete incorporating metakaolin and fly ash. Adv Eng Softw 34:663–669

    Article  Google Scholar 

  44. Ince R (2004) Prediction of fracture parameters of concrete by artificial neural networks. Eng Fract Mech 71:2143–2159

    Article  Google Scholar 

  45. Adhikary BB, Mutsuyoshi H (2006) Prediction of shear strength of steel fiber RC beams using neural networks. Construct Build Mater 20:801–811

    Article  Google Scholar 

  46. Kewalramani MA, Gupta R (2006) Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Autom Constr 15:374–379

    Article  Google Scholar 

  47. Pala M, Özbay E, Öztaş A, Yuce MI (2007) Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Construct Build Mater 21:384–394

    Article  Google Scholar 

  48. Topcu IB, Sarıdemir M (2007) Prediction of properties of waste AAC aggregate concrete using artificial neural network. Comput Mater Sci 41:117–125

    Article  Google Scholar 

  49. Topçu İB, Sarıdemir M (2008) Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Construct Build Mater 22:532–540

    Article  Google Scholar 

  50. Topcu IB, Sarıdemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 41:305–311

    Article  Google Scholar 

  51. Sarıdemir M (2009) Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks. Adv Eng Softw 40:350–355

    Article  MATH  Google Scholar 

  52. Park JH, Shin SW, Kim SY (2018) Traffic volume dependent displacement estimation model for Gwangan bridge using monitoring big data. J Korean Soc Civ Eng 38:183–194

    Google Scholar 

  53. Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by a Grant (20CTAP-C152286-02) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean Government.

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Authors and Affiliations

  1. Nuclear Fuel Cycle Post-Irradiation Examination Division, Korea Atomic Energy Research Institute, 111, Daedeok-daero 989 beon-gil, Yuseong-gu, Daejeon, 34057, Republic of Korea

    Hyun Su Moon

  2. Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291, Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea

    Young Kwang Hwang

  3. Department of Civil and Environmental Engineering, College of Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea

    Moon Kyum Kim & Yun Mook Lim

  4. R&D Planning Office, Korea Expressway Corporation Research Institute, 922, Dongbu-daero, Hwaseong-si, Gyeonggi-do, 18489, Republic of Korea

    Hyeong-Taek Kang

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  1. Hyun Su Moon
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Correspondence to Yun Mook Lim.

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Moon, H.S., Hwang, Y.K., Kim, M.K. et al. Application of artificial neural network to predict dynamic displacements from measured strains for a highway bridge under traffic loads. J Civil Struct Health Monit 12, 117–126 (2022). https://doi.org/10.1007/s13349-021-00531-7

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  • DOI: https://doi.org/10.1007/s13349-021-00531-7

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