LSTM-BASED DAMAGE PREDICTION AND ASSESSMENT OF SPATIAL FRAME STRUCTURE

YANG Yuan; CUI Qiandao; LIAN Jijian; LIU Hongbo; ZHOU Guangen; CHEN Zhihua

doi:10.13204/j.gyjzG20092308

Volume 51 Issue 7

Nov. 2021

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2021 > 51(7): 203-208

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

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LSTM-BASED DAMAGE PREDICTION AND ASSESSMENT OF SPATIAL FRAME STRUCTURE

doi: 10.13204/j.gyjzG20092308

1. School of Civil Engineering, Tianjin University, Tianjin 300072, China;
2. Zhejiang Southeast Space Frame Co., Ltd., Hangzhou 311209, China;
3. Tianjin International Engineer Institute, Tianjin University, Tianjin 300072, China;
4. College of Water Conservancy and Hydropower, Hebei University of Engineering, Handan 056021, China

Received Date: 2020-09-23
Available Online: 2021-11-11

Abstract

Abstract

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.
- spatial frame structure,
- LSTM,
- damage prediction and assessment,
- deep learning

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References(12)

References

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