A novel recursive stochastic subspace identification algorithm with its application in long-term structural health monitoring of office buildings

  • Wu, Wen-Hwa (Department of Construction Engineering, National Yunlin University of Science and Technology) ;
  • Jhou, Jhe-Wei (Department of Construction Engineering, National Yunlin University of Science and Technology) ;
  • Chen, Chien-Chou (Department of Construction Engineering, National Yunlin University of Science and Technology) ;
  • Lai, Gwolong (Department of Construction Engineering, National Yunlin University of Science and Technology)
  • Received : 2019.01.11
  • Accepted : 2019.06.23
  • Published : 2019.10.25


This study develops a novel recursive algorithm to significantly enhance the computation efficiency of a recently proposed stochastic subspace identification (SSI) methodology based on an alternative stabilization diagram. Exemplified by the measurements taken from the two investigated office buildings, it is first demonstrated that merely one sixth of computation time and one fifth of computer memory are required with the new recursive algorithm. Such a progress would enable the realization of on-line and almost real-time monitoring for these two steel framed structures. This recursive SSI algorithm is further applied to analyze 20 months of monitoring data and comprehensively assess the environmental effects. It is certified that the root-mean-square (RMS) response can be utilized as an excellent index to represent most of the environmental effects and its variation strongly correlates with that of the modal frequency. More detailed examination by comparing the monthly correlation coefficient discloses that larger variations in modal frequency induced by greater RMS responses would typically lead to a higher correlation.


Supported by : Ministry of Science and Technology of Republic of China


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