Artificial Intelligence-Driven Active Tuned Mass Damper for Enhanced Seismic Resilience of Shear Frame Smart Structures

This study investigates the application of Artificial Neural Networks (ANNs) for controlling Active Tuned Mass Dampers (ATMDs) in seismic response reduction. The objective is to develop an AI-based ANN controller that effectively reduces structural vibrations during earthquakes. This approach offers a key advantage: achieving good response reduction with fewer sensors compared to traditional methods like Linear Quadratic Regulators (LQR), leading to increased practicality and cost-effectiveness.

A supervised learning approach with the Levenberg–Marquardt backpropagation algorithm trains the ANN controller. The performance of the ANN-controlled ATMD is compared with that of an LQR-controlled system. Additionally, the ANN controller's robustness under signal time delay and noise contamination is evaluated. The ATMD with both controllers is implemented on a 10-story benchmark building subjected to near-field and far-field seismic records.

The obtained results indicate significant reductions in peak displacement, acceleration, velocity, inter-story drift, maximum drift, base shear, and structural energy. Notably, the ANN controller achieves this with a reduced sensor requirement compared to the LQR method. Further, the ANN showed good robustness against signal time delay and noise contamination.

ANNs demonstrated a high potential for controlling ATMDs for seismic response reduction due to their effectiveness and reduced sensor requirements, making them a conceivably more practical and cost-effective solution.