  To maintain structural integrity and functionality during the designed life
cycle of a structure, engineers are expected to accommodate for natural hazards
as well as operational load levels. Active control systems are an efficient
solution for structural response control when a structure is subjected to
unexpected extreme loads. However, development of these systems through
traditional means is limited by their model dependent nature. Recent
advancements in adaptive learning methods, in particular, reinforcement
learning (RL), for real-time decision making problems, along with rapid growth
in high-performance computational resources, help structural engineers to
transform the classic model-based active control problem to a purely
data-driven one. In this paper, we present a novel RL-based approach for
designing active controllers by introducing RL-Controller, a flexible and
scalable simulation environment. The RL-Controller includes attributes and
functionalities that are defined to model active structural control mechanisms
in detail. We show that the proposed framework is easily trainable for a five
story benchmark building with 65% reductions on average in inter story drifts
(ISD) when subjected to strong ground motions. In a comparative study with LQG
active control method, we demonstrate that the proposed model-free algorithm
learns more optimal actuator forcing strategies that yield higher performance,
e.g., 25% more ISD reductions on average with respect to LQG, without using
prior information about the mechanical properties of the system.
