Mastering seismic time series response predictions using an attention-Mamba transformer model for bridge bearings and piers across varied testing conditions

Abstract

This research introduces an advanced method for predicting seismic responses and hysteresis curves of instrumented bridge piers and bearings under various loading conditions, leaning solely on a single deep learning architecture and the same hyperparameters tuning. Test specimens are subjected to ground accelerations including vertical seismic loads and axial forces. To accurately capture peak values, particularly on the negative side of the hysteresis loop (unloading region), the model employs a stacked deep architecture. A key component to overcome the challenges is the self-attention-Mambadriven transformer layer, which enhances the model's ability to capture long-range dependencies in seismic data. This layer works in conjunction with other deep learning techniques to ensure robust and precise predictions. Implemented with Python's Keras functional API, the model processes inputs like ground accelerations, actuator loads, effective height, moment of inertia, and superstructure mass. The model is evaluated with a dataset of 95 real-time hybrid simulation (RTHS) tests for lead rubber bearings, 29 RTHS tests for bridge piers, and 17 cyclic tests (10 fast and 7 slow). Extensive hyperparameter tuning demonstrates the model's proficiency to capture hysteresis and residual deformations accurately. Achieving an impressive correlation with experimentally measured values, ranging from 88.1 to 98.9%, and a reasonable dissipated energy error ratio are notable. The deep learning model reduces the need for additional tests, offering time and cost savings, and provides rapid, and accurate insights into bridge behavior. This supports timely and precise bridge design and aids decision-makers during emergencies.