Intelligent Control of Semi-Active Friction Dampers for Seismic Control of Buildings: A Deep Reinforcement Learning Approach

Semi-active friction dampers (SAFDs), which can adaptively adjust the friction force, function as flexible semi-active control systems for the seismic protection of structures, including buildings exposed to strong ground accelerations. The effectiveness of SAFDs in dissipating seismic energy is highly dependent on the control strategy used to adjust the friction force of dampers. Due to the highly nonlinear and complex nature of buildings equipped with SAFDs, achieving optimal control is challenging and requires advanced adaptive control techniques. In this paper, we propose an intelligent control method based on deep reinforcement learning for the seismic control of buildings with SAFDs. We examine the potential of SAFDs to improve the seismic response of multi-story buildings and present a numerical case study to demonstrate the effectiveness of the proposed intelligent method. In this case study, the performance of the proposed intelligent control method is compared with two optimization methods and a reference controller: (i) friction dampers (FDs) using a constant friction force that is calibrated using a heuristic approach, (ii) FDs whose friction force is optimized through a Genetic Algorithm (GA), and (iii) SAFDs employing a rule-based approach for adaptive friction force adjustment. It was observed that the proposed DRL approach outperformed the alternatives, for example, achieving up to 63.7 % improvement over the rule-based controller. The core contribution of this study is the formulation of an MDP and reward-design framework that enables DRL to perform real-time friction control in SAFDs integrated into multi-story buildings.