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This study introduces a deep Bayesian convolutional neural network for predicting seismic responses. The novel method accurately forecasts structural acceleration and displacement, outperforming conventional models in seismic analysis.

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Area of Science:

  • Structural Engineering
  • Computational Mechanics
  • Artificial Intelligence

Background:

  • Seismic response prediction is critical for structural safety throughout a structure's lifecycle.
  • Conventional neural networks struggle with the inherent randomness in structural dynamics.
  • Deep learning offers potential for enhanced structural response prediction.

Purpose of the Study:

  • To propose a deep Bayesian convolutional neural network (CNN) for accurate seismic response prediction.
  • To address the limitations of deterministic models in capturing random structural dynamics.
  • To validate the model's performance using a three-dimensional building structure.

Main Methods:

  • Development of a deep Bayesian CNN model.
  • Application of the Bayes-backpropagation algorithm for model training.
  • Validation using a numerical example of a 3D building structure and comparison with finite element analysis (FEA).

Main Results:

  • The proposed Bayesian deep learning model accurately predicts both acceleration and displacement responses.
  • Key statistical indices from the model closely match FEA results.
  • The model demonstrates robustness and accounts for the influence of random parameters.

Conclusions:

  • Deep Bayesian CNNs provide a powerful tool for reliable seismic response prediction.
  • The Bayes-backpropagation algorithm effectively trains the proposed deep learning model.
  • This approach enhances the accuracy and robustness of predicting structural behavior under seismic loads.