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<p>The destructive mechanisms of earthquakes on engineering structures are highly complex, and developing high-precision response prediction models is a critical scientific issue for enhancing the seismic resilience of infrastructure. This paper innovatively proposes a hybrid deep learning architecture based on a stacked long short-term memory network combined with a Transformer (ST-LSTM). By constructing a spatiotemporal feature fusion mechanism, the model significantly improves the accuracy of structural seismic response prediction. To systematically validate the model’s performance, two typical engineering cases were selected for comparative analysis: first, a six-story concrete hotel building in the United States was studied to thoroughly analyze its historical seismic displacement response characteristics; second, a 426-m-span rigid frame bridge was targeted to predict its nonlinear curvature response behavior. The results indicate that the proposed ST-LSTM model significantly outperforms traditional long short-term memory models in terms of computational efficiency and prediction accuracy. In the two cases, the model’s coefficient of determination (<i>R</i><sup>2</sup>) reached 0.975 and 0.983, respectively, with the predicted peak error controlled within 5% and 7%. The research findings provide new technical means for real-time health monitoring and intelligent seismic assessment of engineering structures, holding significant theoretical value and engineering significance for enhancing the seismic resilience of infrastructure.</p>

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Deep learning models fusing transformer-stacked long short-term memory: An efficient prediction method for nonlinear seismic response of buildings and bridges

  • 期刊论文

  • 发表日期：

    2025年12月18日

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  • Chang He,
  • Gao Zhang,
  • Jin Zhang,
  • Zhongyao Zhang

摘要

The destructive mechanisms of earthquakes on engineering structures are highly complex, and developing high-precision response prediction models is a critical scientific issue for enhancing the seismic resilience of infrastructure. This paper innovatively proposes a hybrid deep learning architecture based on a stacked long short-term memory network combined with a Transformer (ST-LSTM). By constructing a spatiotemporal feature fusion mechanism, the model significantly improves the accuracy of structural seismic response prediction. To systematically validate the model’s performance, two typical engineering cases were selected for comparative analysis: first, a six-story concrete hotel building in the United States was studied to thoroughly analyze its historical seismic displacement response characteristics; second, a 426-m-span rigid frame bridge was targeted to predict its nonlinear curvature response behavior. The results indicate that the proposed ST-LSTM model significantly outperforms traditional long short-term memory models in terms of computational efficiency and prediction accuracy. In the two cases, the model’s coefficient of determination (R2) reached 0.975 and 0.983, respectively, with the predicted peak error controlled within 5% and 7%. The research findings provide new technical means for real-time health monitoring and intelligent seismic assessment of engineering structures, holding significant theoretical value and engineering significance for enhancing the seismic resilience of infrastructure.