Simultaneous Optimization of the Number and Distribution of Friction Dampers for Seismically Excited Buildings: An AI Approach

Friction dampers are an economical and effective passive control solution for enhancing the seismic performance of multi-story buildings. Their performance strongly depends on their configuration (distribution or placement) in the building, and from a cost-effectiveness standpoint, the minimum feasible number of dampers should be installed. Therefore, addressing both of these aspects simultaneously is essential. However, the associated optimization problem is a large-scale, non-convex, combinatorial optimization problem. In this paper, we present a deep reinforcement learning-based solution for simultaneous optimization of the number and distribution of friction dampers in multi-story buildings for seismic protection. The presented solution is computationally efficient and is tested on a case study to demonstrate its performance.