Neural network based active control of base isolated structure considering isolator nonlinearity

Abstract

Introduction: Hybrid control systems combining passive and active strategies have emerged as effective solutions to enhance structural resilience during earthquakes. Recent advancements in smart structures integrate active tuned mass dampers (ATMDs) for precise dynamic response control and seismic hazard mitigation. Simultaneously, artificial intelligence (AI), particularly machine learning algorithms, has opened new frontiers in structural control. Methods: This study proposes a novel AI-based approach for structures equipped with nonlinear base isolation and an ATMD. An artificial neural network (ANN) is employed, trained via supervised learning using the Levenberg-Marquardt backpropagation algorithm to minimize displacement demands during strong earthquakes. The ANN-driven controller aims to achieve significant response reduction with fewer sensors than traditional algorithms while enhancing robustness against signal time delays and white noise contamination. For validation, an ATMD is installed at the base isolation layer of an 8-story benchmark building. The ANN controller's performance is evaluated under near-field and far-field seismic excitations and compared with a conventional linear quadratic regulator (LQR)-controlled ATMD and a classical tuned mass damper (TMD). Robustness tests include time delays and white noise in input signals. Results: The results demonstrate that the ANN-driven ATMD controller notably reduces key dynamic response parameters, including peak base acceleration, displacement, velocity, inter-story drift, maximum drift, and base shear, under both near-field and far-field earthquake scenarios. Furthermore, the ANN controller maintains high performance even when subjected to signal time delays and white noise contamination, underscoring its robustness. Importantly, these improvements are attained while utilizing fewer sensors than the LQR controller, highlighting the practicality and cost-effectiveness of the proposed method. Discussion: The proposed ANN controller achieves performance comparable to the full-state LQR controller but requires fewer sensors, enhancing practicality and cost-effectiveness for real-world applications. This approach demonstrates superior robustness against signal imperfections while maintaining high seismic response mitigation efficacy.