Attention-based hybrid convolutional-long short-term memory network for bridge pier hysteresis and backbone curves prediction

Abstract

This paper proposes a solution to the problem of automatically predicting hysteresis and backbone curves of bridge piers under seismic loads. The proposed solution utilizes a stacked hybrid Convolutional Neural Network-bidirectional Cuda Deep Neural Network Long Short Term Memory layer benefiting from the skip connections technique and incorporates a custom task-specific attention layer to enhance its performance. The proposed framework borrows the functional API provided by the Keras library in Python to construct a model taking into account horizontal and vertical ground accelerations, actuator loads in both horizontal and vertical directions, the effective pier height, the second moment of area, and the superstructure mass as input features. The deep learning model demands a substantial amount of data for effective training, validation, and testing. An error-sensitive analysis suggests that a comprehensive dataset should consist of a minimum of 12 sets of pier data for real-time hybrid simulations and 17 sets for cyclic experiments (10 for high-speed and seven for low-speed scenarios). This extensive dataset is deemed essential for the optimal performance of the model. The same deep learning framework and optimization of hyperparameters apply when training real-time hybrid simulations and conducting cyclic tests. After 5000 epochs, the proposed hybrid loss function, combining mean square and mean absolute errors, exhibits a steady and gradual decrease toward near-zero values within the datasets used for training and validation. Additionally, over 93% correlation exists between the predicted unseen time series responses and those derived from empirical measurements. Overall, the proposed deep learning model offers significant advantages, notably in terms of time and cost savings associated with experimental endeavors for new tests. By providing a rapid and accurate understanding of the hysteretic behavior of bridge piers, this model contributes to more efficient bridge design processes. Ultimately, it facilitates precision in design decisions, leading to enhanced accuracy and effectiveness in bridge engineering.