LSTM, WaveNet, and 2D CNN for nonlinear time history prediction of seismic responses

Ning, Chunxiao; Xie, Yazhou; Sun, Lijun

doi:10.1016/j.engstruct.2023.116083

Cited by 27 publications

(3 citation statements)

References 46 publications

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“…For instance, Ref. [33] utilized LSTMs, alongside WaveNet and 2D CNN, for nonlinear time history prediction of seismic responses. Similarly, Ref.…”

Section: Long Short-term Memorymentioning

confidence: 99%

A Comparative Analysis of Slope Failure Prediction Using a Statistical and Machine Learning Approach on Displacement Data: Introducing a Tailored Performance Metric

Chaulagain,

Choi,

Kim

et al. 2023

Buildings

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Slope failures pose significant threats to human safety and vital infrastructure. The urgent need for the accurate prediction of these geotechnical events is driven by two main goals: advancing our understanding of the underlying geophysical mechanisms and establishing efficient evacuation protocols. Although traditional physics-based models offer in-depth insights, their reliance on numerous assumptions and parameters limits their practical usability. In our study, we constructed an experimental artificial slope and monitored it until failure, generating an in-depth displacement dataset. Leveraging this dataset, we developed and compared prediction models rooted in both statistical and machine learning paradigms. Furthermore, to bridge the gap between generic evaluation metrics and the specific needs of slope failure prediction, we introduced a bespoke performance. Our results indicate that while the statistical approach did not effectively provide early warnings, the machine learning models, when assessed with our bespoke performance metric, showed significant promise as reliable early warning systems. These findings hold potential to fortify disaster prevention measures and prioritize human safety.

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“…For instance, Ref. [33] utilized LSTMs, alongside WaveNet and 2D CNN, for nonlinear time history prediction of seismic responses. Similarly, Ref.…”

Section: Long Short-term Memorymentioning

confidence: 99%

A Comparative Analysis of Slope Failure Prediction Using a Statistical and Machine Learning Approach on Displacement Data: Introducing a Tailored Performance Metric

Chaulagain,

Choi,

Kim

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

“…Similarly, research efforts have focused on developing multivariate surrogate models through, for instance, the polynomial response surface method, 18 support vector machines, 19 artificial neural networks, 20 and deep learning 21 . Time‐series surrogate models have also been developed using convolutional neural networks 22 and recurrent neural networks 23 to predict the entire seismic response histories.…”

Section: Introductionmentioning

confidence: 99%

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

Ning,

Xie,

Burton

et al. 2024

Earthq Engng Struct Dyn

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Regional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios.

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“…[31][32][33] In recent years, applications of various deep learning models such as multilayer perceptron (MLP) networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and attention-based networks in seismic dynamic response modeling have achieved significant progress. 32,[34][35][36][37] Zhang et al 33 introduced a stacked long short-term memory (LSTM) model for nonlinear structural response modeling and prediction. Taking the ground motion sequence as input, the model can achieve accurate structural response sequence prediction.…”

Section: Introductionmentioning

confidence: 99%

Multi‐input integrative neural network for soil seismic response modeling at KiK‐net downhole array sites

Li,

Jin,

Huang

et al. 2024

Earthq Engng Struct Dyn

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Prediction of the soil seismic response is of primary importance for geotechnical earthquake engineering. Conventional physics‐based models such as the finite element method (FEM) often face challenges due to inherent model assumptions and uncertainties of model parameters. Furthermore, these physics‐based models require significant computational resources, particularly when simulating seismic responses across numerous soil sites. In this study, a multi‐ input integrative neural network is developed for predicting soil seismic response based on the recorded data from a large number of downhole array sites. Ground motions, seismic event information, and wave velocity structures of the sites are utilized as input data in the proposed neural network, enabling the model to adapt to various site conditions. Comparative assessments against state‐of‐the‐art FEM models demonstrate that the proposed models exhibit superior prediction performance with increased efficiency. Furthermore, the pre‐training technique, a transfer learning method, is employed to predict the seismic response at new stations. By fine‐tuning the pre‐trained model derived from the extensive dataset with limited recorded data from new stations, high‐precision seismic response predictions can be realized, illustrating the adaptability and efficacy of the proposed approach in data‐scarce conditions.

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LSTM, WaveNet, and 2D CNN for nonlinear time history prediction of seismic responses

Cited by 27 publications

References 46 publications

A Comparative Analysis of Slope Failure Prediction Using a Statistical and Machine Learning Approach on Displacement Data: Introducing a Tailored Performance Metric

A Comparative Analysis of Slope Failure Prediction Using a Statistical and Machine Learning Approach on Displacement Data: Introducing a Tailored Performance Metric

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

Multi‐input integrative neural network for soil seismic response modeling at KiK‐net downhole array sites

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