Structural damage diagnosis and fine scale finite element intelligence simulation of long span cable stayed bridges



According to the accumulation characteristics presented by the security threat factors existed in building structure, it is relatively difficult to carry out real-time danger monitoring. In addition, the noise and structure will form complex interference and make the real-time monitoring more difficult. Therefore, this paper proposes one bridge structure monitoring algorithm of restricted Boltzmann machine (SDRBM) based on sparse cross-entropy penalty factor. Firstly, the improvement of deep network learning process based on sparse cross-entropy penalty factor and restricted Boltzmann machine (RBM) has effectively resolved the homogenization problems existed in deep network learning process; secondly, the preset rough set is used to make pretreatment for input bridge health signals to achieve complete preservation of data information and balance of effective reduction as well as simplify treatment complexity; finally, the experiment shows that the proposed DRBM bridge structure monitoring algorithm of sparse cross-entropy penalty factor can achieve bridge safety monitoring under the condition of unknown noise and structure.


Boltzmann machine Finite element simulation Bridge structure Fine scale Damage diagnosis 



Open project of Shanghai Key Laboratory of Engineering Structure Safety (No. 2015-KF05).


  1. 1.
    Zhang, X., Jin, X., Chen, X.: Simulation of the interactions between a train and a long-span cable-stayed bridge using parallel computing with domain decomposition. Proceed. Inst. Mech. Eng. Part F 226(4), 347–359 (2012)CrossRefGoogle Scholar
  2. 2.
    Naderian, H., Cheung, M.M.S., Shen, Z., et al.: Integrated finite strip analysis for long-span cable-stayed bridges. Comput. Struct. 158, 82–97 (2015)CrossRefGoogle Scholar
  3. 3.
    Zhou, R., Zong, Z.H., Huang, X.Y., et al.: Seismic response study on a multi-span cable-stayed bridge scale model under multi-support excitations. Part II: numerical analysis. J. Zhejiang Univ.-Sci. A 15(6), 405–418 (2014)CrossRefGoogle Scholar
  4. 4.
    Li, S., Li, H., Liu, Y., et al.: SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge. Struct. Control Health Monit. 21(2), 156–172 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Kong, X., Wu, D.J., Cai, C.S., et al.: New strategy of substructure method to model long-span hybrid cable-stayed bridges under vehicle-induced vibration. Eng. Struct. 34(1), 421–435 (2012)CrossRefGoogle Scholar
  6. 6.
    Huang, B., Seresh, R.F., Zhu, L.: Statistical analysis of basic dynamic characteristics of large span cable-stayed bridge based on high order perturbation stochastic FEM. Adv. Struct. Eng. 16(9), 1499–1512 (2013)CrossRefGoogle Scholar
  7. 7.
    Hu, Z.J., Zhang, M.H., Kong, X.S., et al.: Static and dynamic experimental model for long-span steel-truss arch bridges. Zhongguo Gonglu Xuebao/China J. Highw. Transp. 27(9), 82–89 (2014)Google Scholar
  8. 8.
    Fujino, Y., Siringoringo, D.: Vibration mechanisms and controls of long-span bridges: a review. Struct. Eng. Int. J. Int. Assoc. Bridge Struct. Eng. 23(3), 2013 (2013)Google Scholar
  9. 9.
    Zong, Z.H., Zhou, R., Huang, X.Y., et al.: Seismic response study on a multi-span cable-stayed bridge scale model under multi-support excitations. Part I: shaking table tests. J. Zhejiang Univ. Sci. A 15(5), 351–363 (2014)CrossRefGoogle Scholar
  10. 10.
    Zhao, Q.: Dynamic model updating of long-span bridge based on optimization design principle under environment excitation. In: International Conference on Consumer Electronics, Communications and Networks. IEEE, pp. 1178–1181. (2011)Google Scholar
  11. 11.
    Camara, A., Astiz, M.A.: Pushover analysis for the seismic response prediction of cable-stayed bridges under multi-directional excitation. Eng. Struct. 41(3), 444–455 (2012)CrossRefGoogle Scholar
  12. 12.
    Wei, X., Qiang, S.: Limit bearing capacity of longitudinal clapboard of long-span single pylon cable-stayed bridge. Xinan Jiaotong Daxue Xuebao/J. Southwest Jiaotong Univ. 47(1), 57–62 (2012)Google Scholar
  13. 13.
    Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recognit. Lett. 94, 112–117 (2017)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Faculty of Civil Engineering and MechanicsKunming University of Science and TechnologyKunmingChina
  2. 2.Shanghai Key Laboratory of Engineering Structure SafetyShanghaiChina

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