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YANG Yuan, CUI Qiandao, LIAN Jijian, LIU Hongbo, ZHOU Guangen, CHEN Zhihua. LSTM-BASED DAMAGE PREDICTION AND ASSESSMENT OF SPATIAL FRAME STRUCTURE[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(7): 203-208. doi: 10.13204/j.gyjzG20092308
Citation: YANG Yuan, CUI Qiandao, LIAN Jijian, LIU Hongbo, ZHOU Guangen, CHEN Zhihua. LSTM-BASED DAMAGE PREDICTION AND ASSESSMENT OF SPATIAL FRAME STRUCTURE[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(7): 203-208. doi: 10.13204/j.gyjzG20092308

LSTM-BASED DAMAGE PREDICTION AND ASSESSMENT OF SPATIAL FRAME STRUCTURE

doi: 10.13204/j.gyjzG20092308
  • Received Date: 2020-09-23
    Available Online: 2021-11-11
  • The Kiewitt (K6) spherical reticulated shell was used as the research object to study the data-driven damage prediction and assessment of the space frame structure. By numerical simulations, Structural Health Monitoring (SHM) simulation data of structural modal frequency subjected to uniform atmospheric corrosion were obtained. A Long-Short-Term Memory (LSTM) based the deep learning model for structural damage prediction and assessment was constructed. Finally, the LSTM-based damage prediction and assessment method for the space frame structure was summarized. The results showed that LSTM could be used to establish a data-driven deep learning model for SHM data, predict and assess the structural health status. The model performed well on the simulation data with good anti-noise properties which could nicely fit SHM simulation data with a good short-term prediction effect. The updated data sets could readjust the model, so as to achieve the continuous prediction and assessment for structural health status.
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