Long short-term memory-enhanced semi-active control of cable vibrations with a magnetorheological damper

The large vibrations of stay cables pose significant challenges to the structural performance and safety of cable-stayed bridges. While magnetorheological dampers (MRDs) have emerged as an effective solution for suppressing these vibrations, establishing accurate forward and inverse mapping models for MRDs to facilitate effective semi-active control of cable vibrations remains a formidable task. To address this issue, the current study proposes an innovative strategy that leverages Long Short-Term Memory (LSTM) neural networks for MRD modeling, thus enhancing semi-active control of stay cable vibrations. A high-fidelity data set accurately capturing the MRD dynamics is first generated by coupling finite element analysis and computational fluid dynamic approach. The obtained data set is then utilized for training LSTM-based forward and inverse mapping models of MRD. These LSTM models are subsequently integrated into dynamic computational models for effectively suppressing the stay cable vibrations, culminating in an innovative semi-active control strategy. The feasibility and superiority of the proposed strategy are demonstrated through comprehensive comparative analyses with existing passive, semi-active and active control methodologies involving sinusoidal load, Gaussian white noise load and rain-wind induced aerodynamic load scenarios, paving the way for novel solutions in semi-active vibration control of large-scale engineered structures.