Smart bridge bearing monitoring: Predicting seismic responses with a multi-head attention-based CNN-LSTM network

Abstract

This paper introduces a novel method to spontaneously predict displacement time histories and hysteresis curves of bridge lead rubber bearings under seismic loads and axial forces. The method leverages a stacked convolutional-bidirectional Cuda Long Short Term Memory network, enhanced with multi-head attention, skip connections, exponential learning rate scheduler, and a hybrid activation function to improve performance. The framework utilizes the functional application programming interface provided by the Python Keras library to build a model that takes input features such as horizontal and vertical ground accelerations, actuator loads in both lateral and vertical directions, and the superstructure mass. The effectiveness of the deep learning model is evaluated using a considerable experimental dataset of 53 real-time hybrid simulations, spanning various earthquake intensities and superstructure masses (Chi-Chi: 15 scenarios, El Centro: 15 scenarios, Kobe: 13 scenarios, and Northridge: 10 scenarios). Initially, Northridge earthquake data serves as unseen data, while the rest is used for training and validation. In a subsequent trial, the unseen data is centered on Kobe earthquake scenarios. By employing a hybrid loss function merging mean square and mean absolute errors, the model exhibits a substantial correlation of over 83% between predicted displacement time series and empirical measurements for the unseen data. In summary, the proposed model offers miscellaneous benefits, including time and cost savings in experimental efforts by decreasing the need for additional tests. It further delivers a swift and precise insight into the bridge bearing performance and its energy dissipation, facilitating timely and accurate bridge design in different scenarios for engineers.