Steel and Composite Structures

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Seismic response study of tower-line system considering bolt slippage under foundation displacement

  • Jia-Xiang Li (School of Resources and Civil Engineering, Northeastern University) ;
  • Jin-Peng Cheng (School of Resources and Civil Engineering, Northeastern University) ;
  • Zhuo-Qun Zhang (State Grid Economic and Technological Research Institute Co., Ltd.) ;
  • Chao Zhang (School of Resources and Civil Engineering, Northeastern University)
  • Received : 2024.04.18
  • Accepted : 2024.07.04
  • Published : 2024.07.25
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Abstract

Once the foundation displacement of the transmission tower occurs, additional stress will be generated on the tower members, which will affect the seismic response of transmission tower-line systems (TTLSs). Furthermore, existing research has shown that the reciprocating slippage of joints needs to be considered in the seismic analysis. The hysteretic behavior of joints is obtained by model tests or numerical simulations, which leads to the low modeling efficiency of TTLSs. Therefore, this paper first utilized numerical simulation and model tests to construct a BP neural network for predicting the skeleton curve of joints, and then a numerical model for a TTLS considering the bolt slippage was established. Then, the seismic response of the TTLS under foundation displacement was studied, and the member stress changes and the failed member distribution of the tower were analyzed. The influence of foundation displacement on the seismic performance were discussed. The results showed that the trained BP neural network could accurately predict the hysteresis performance of joints. The slippage could offset part of the additional stress caused by foundation settlement and reduce the stress of some members when the TTLS with foundation settlement was under earthquakes. The failure members were mainly distributed at the diagonal members of the tower leg adjacent to the foundation settlement and that of the tower body. To accurately analyze the seismic performance of TTLSs, the influence of foundation displacement and the joint effect should be considered, and the BP neural network can be used to improve modeling efficiency.

Keywords

Acknowledgement

This research was supported by the Fundamental Research Funds for the Central Universities (Grant No. N2301012), and State Key Laboratory of Coastal and Offshore Engineering Fund (LP2218).

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