Recursive long short-term memory network for predicting nonlinear structural seismic response
Z Xu, J Chen, J Shen, M Xiang - Engineering Structures, 2022 - Elsevier
Artificial neural networks have been used to predict nonlinear structural time histories under
seismic excitation because they have a significantly lower computational cost than the …
seismic excitation because they have a significantly lower computational cost than the …
Deep long short-term memory networks for nonlinear structural seismic response prediction
This paper presents a comprehensive study on developing advanced deep learning
approaches for nonlinear structural response modeling and prediction. Two schemes of the …
approaches for nonlinear structural response modeling and prediction. Two schemes of the …
LSTM, WaveNet, and 2D CNN for nonlinear time history prediction of seismic responses
Predicting the nonlinear time-history responses of civil engineering structures under seismic
loading remains an essential task in earthquake engineering. This paper explores the …
loading remains an essential task in earthquake engineering. This paper explores the …
Seismic response prediction of RC bridge piers through stacked long short-term memory network
This paper aims at addressing the prediction of the displacement time histories and
subsequently hysteresis curves of reinforced concrete bridge piers using a real-time hybrid …
subsequently hysteresis curves of reinforced concrete bridge piers using a real-time hybrid …
Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks
Early and accurate damage evaluation after earthquakes is critical for planning an efficient
and timely emergency response. State-of-the-art rapid evaluation techniques of structural …
and timely emergency response. State-of-the-art rapid evaluation techniques of structural …
Attention-based LSTM (AttLSTM) neural network for seismic response modeling of bridges
Accurate prediction of bridge responses plays an essential role in health monitoring and
safety assessment of bridges subjected to dynamic loads such as earthquakes. To this end …
safety assessment of bridges subjected to dynamic loads such as earthquakes. To this end …
Real‐time regional seismic damage assessment framework based on long short‐term memory neural network
Effective post‐earthquake response requires a prompt and accurate assessment of
earthquake‐induced damage. However, existing damage assessment methods cannot …
earthquake‐induced damage. However, existing damage assessment methods cannot …
A long short-term memory based deep learning algorithm for seismic response uncertainty quantification
A Kundu, S Ghosh, S Chakraborty - Probabilistic Engineering Mechanics, 2022 - Elsevier
The application of metamodeling technique to overcome computational challenge of Monte
Carlo simulation (MCS) technique for response uncertainty quantification under stochastic …
Carlo simulation (MCS) technique for response uncertainty quantification under stochastic …
PI-LSTM: Physics-informed long short-term memory network for structural response modeling
Deep learning models have achieved remarkable accuracy for structural response
modeling. However, these models heavily depend on having a sufficient amount of training …
modeling. However, these models heavily depend on having a sufficient amount of training …
Time series estimation based on deep learning for structural dynamic nonlinear prediction
H Peng, J Yan, Y Yu, Y Luo - Structures, 2021 - Elsevier
This paper explores state-of-the-art deep learning techniques to analyse and predict
structural dynamic nonlinear behaviours in civil engineering applications. In this paper, three …
structural dynamic nonlinear behaviours in civil engineering applications. In this paper, three …