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

Article

Abstract

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.

Keywords

Boltzmann machine Finite element simulation Bridge structure Fine scale Damage diagnosis 

Notes

Acknowledgements

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

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

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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