Dynamic Response-Based Safety Monitoring and Damage Identification of Concrete Arch Dams via PSO–LSTM

TL;DR

This paper introduces a PSO-LSTM model to monitor and detect damage in concrete arch dams using dynamic responses during earthquakes.

Contribution

A novel PSO-LSTM model is proposed for improved safety monitoring and damage identification in arch dams.

Findings

The PSO-LSTM model outperformed standalone LSTM and SSA-LSTM in monitoring accuracy.

The method effectively identified structural damage using residual analysis and 3σ criterion.

Validation via simulations and shaking table tests confirmed the model's effectiveness.

Abstract

The measured dynamic response of concrete arch dams under seismic excitation is a typical time series that contains rich information about structural conditions. Safety monitoring based on dynamic responses of arch dam structures is highly important for the timely detection of structural damage and ensuring dam safety. In this study, a PSO-LSTM-based model for safety monitoring and damage identification of arch dam structures was proposed. The method was centered on the long short-term memory (LSTM) neural network, and key hyperparameters were adaptively tuned by the particle swarm optimization (PSO) algorithm to improve monitoring accuracy for nonlinear and nonstationary structural dynamic responses. Structural damage was identified through residual analysis combined with the 3σ anomaly detection criterion. Numerical simulations and shaking table model test cases of an arch dam were introduced for validation. The proposed method was compared with the standalone LSTM model and the SSA-LSTM model in terms of the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and damage identification accuracy. The results showed that the proposed PSO-LSTM method achieved greater accuracy in monitoring the safety of arch dam dynamic responses and effectively identified structural damage, thereby verifying its effectiveness.

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