A STRESS DAMAGE ASSESSMENT METHOD OF STEEL BARS IN CONCRETE STRUCTURES BASED ON MMM

Lei Liu, Qianwen Xia, Ya Li, and Yinghao Qu

References

  1. [1] J.Y. Yang, B.H. Xia, Z. Chen, et al., Vibration-based structuraldamage identification: A review, International Journal ofRobotics and Automation, 35(2), 2020, 123–131.
  2. [2] Z. Chen, X. Zhou, X. Wang, et al., Deployment of a smartstructural health monitoring system for long-span arch bridges:A review and a case study, Sensors, 17(9), 2017, 2151.
  3. [3] Y.Q. Bao, Z.C. Chen, S.Y. Wei, et al., The state of the art ofdata science and engineering in structural health monitoring,Engineering, 5(2), 2019, 234–242.
  4. [4] H. Li, J.P. Ou, X.G. Zhang, et al., Research and practice ofhealth monitoring for long-span bridges in the mainland ofChina, Smart Structures & Systems, 15(3), 2015, 555–576.
  5. [5] J.P. Ou and H. Li, Structural health monitoring in mainlandChina: Review and future trends, Structural Health Monitor-ing, 9(3), 2010, 219–231.
  6. [6] B.F. Spencer, M.E. Ruiz-Sandoval, and N. Kurata, Smartsensing technology: Opportunities and challenges, StructuralControl & Health Monitoring, 11(4), 2010, 349–368.
  7. [7] H. Wang, T.Y. Tao, A.Q. Li, and Y.F. Zhang, Structuralhealth monitoring system for Sutong cable-stayed bridge, SmartStructures & Systems, 18(2), 2016, 317–334.
  8. [8] P.C. Chang, A. Flatau, and S.C. Liu, Health monitoring ofcivil infrastructure, Structural Health Monitoring, 2(3), 2003,257–267.
  9. [9] A.A. Mufti, Structural health monitoring of innovative Cana-dian civil engineering structures, Structural Health Monitoring,1(1), 2002, 89–103.
  10. [10] J.M. Ko and Y.Q. Ni, Technology developments in structuralhealth monitoring of large-scale bridges, Engineering Struc-tures, 27(12), 2005, 1715–1725.
  11. [11] X.H. He, X.G. Hua, Z.Q. Chen, and F.L. Huang, EMD-basedrandom decrement technique for modal parameter identificationof an existing railway bridge, Engineering Structures, 33(4),2011, 1348–1356.
  12. [12] X.H. He, J. Fang, A. Scanlon, and Z.Q. Chen, Wavelet-basednonstationary wind speed model in Dongting Lake cable-stayedbridge, Engineering, 2(11), 2010, 895–903.
  13. [13] H. Chen, M. Kurt, Y.S. Lee, et al., Experimental systemidentification of the dynamics of a vibro-impact beam witha view towards structural health monitoring and damagedetection, Mechanical Systems & Signal Processing, 46(1),2014, 91–113.
  14. [14] Z. Ismail, H. Abdul Razak, and A.G. Abdul Rahman, Deter-mination of damage location in RC beams using mode shapederivatives, Engineering Structures, 28(11), 2006, 1566–1573.
  15. [15] F. Abbassi, T. Belhadj, S. Mistou, and A. Zghal, Parameteridentification of a mechanical ductile damage using artificialneural networks in sheet metal forming, Materials Design, 45,2013, 605–615.
  16. [16] R. Yao and S.N. Pakzad, Autoregressive statistical patternrecognition algorithms for damage detection in civil structures,Mechanical Systems & Signal Processing, 31, 2012, 355–368.
  17. [17] A. Kunwar, R. Jha, M. Whelan, and K. Janoyan, Damagedetection in an experimental bridge model using Hilbert–Huangtransform of transient vibrations, Structural Control & HealthMonitoring, 20(1), 2013, 1–15.
  18. [18] S.S. Patel, A.P. Chourasia, S.K. Panigrahi, et al., Damageidentification of RC structures using wavelet transformation,Procedia Engineering, 144, 2016, 336–342.
  19. [19] S.W. Doebling, C.R. Farrar, M.B. Prime, et al., Damage iden-tification and health monitoring of structural and mechanicalsystems from changes in their vibration characteristics: Aliterature review, 1st ed. (New Mexico: Los Alamos NationalLaboratory Press, 1996).
  20. [20] K.C. Chang and C.W. Kim, Modal-parameter identificationand vibration-based damage detection of a damaged steel trussbridge, Engineering Structures, 122, 2016, 156–173.
  21. [21] R.V. Farahani and D. Penumadu, Damage identification ofa full-scale five-girder bridge using time-series analysis ofvibration data, Engineering Structures, 115, 2016, 129–139.
  22. [22] H. Zhang, J.T. Zhou, R.Q. Zhao, et al., Experimental study ondetection of rebar corrosion in concrete based on metal magneticmemory, International Journal of Robotics and Automation,32(5), 2017, 530–537.
  23. [23] D.C. Jiles, Theory of the magnetomechanical effect, Journalof Physics D-Applied physics, 28(8), 1995, 1537–1546.
  24. [24] J.L. Ren, M.J. Lin, Y.B. Chi, et al., Metal magnetic memorydetection technology, 1st ed. (Beijing: China Electric PowerPress, 2000).
  25. [25] J.T. Zhou, J.L. Qiu, Y.X. Zhou, et al., Experimental studyon residual bending strength of corroded reinforced concretebeam based on micromagnetic sensor, Sensors, 18(8), 2018,2635.
  26. [26] R.C. Xia, J.T. Zhou, H. Zhang, et al., Quantitative study oncorrosion of steel strands based on self-magnetic flux leakage,Sensors, 18(5), 2018, 1396.
  27. [27] J.P. Jiao, Y. Chang, G.H. Li, et al., Study on low frequencymagnetic flux leakage detection technology for internal andexternal surface cracks of ferromagnetic components, Chinesejournal of instrumentation, 37(8), 2016, 1808–1817.
  28. [28] W.L. Jin, J. Zhang, C.S. Chen, et al., A new method for fatiguestudy of reinforced concrete structures based on piezomag-netism, Journal of Building Structures, 37(4), 2016, 133–142.8

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