LAUSR

Abstract Traditionally, structural optimization is a numerical process; candidate designs are created and evaluated through numerical simulation (e.g., finite element analysis). However, when dealing with complex structures that are difficult to model numerically, large errors could exist between the numerical model and the physical structure. In this case, the optimization is less meaningful because the optimal results are associated with the numerical model instead of the physical structure. Experiments can be included in the optimization algorithm to represent complex structures or components. However, the time and cost limitations are prohibitive when iteratively constructing and evaluating complete structural systems. Real-time hybrid simulation (RTHS) is an efficient and cost-effective experimental tool that combines numerical simulation with experimental testing to capture the total structural performance. This paper proposes a framework for real-time hybrid optimization (RTHO); RTHS is used to evaluate the performance of candidate designs within the optimization process. The framework creates a cyber-physical optimization environment using RTHS, a modern experimental technique with roots in earthquake engineering. This paper outlines the framework for RTHO with accompanying proof-of-concept studies. In a preliminary study, the base isolation design of a two-story building was optimized for seismic protection. RTHO was further validated for the optimal selection of multiple semi-active control law parameters for an MR damper installed in the isolation layer of a five-story base-isolated building. Both cases used RTHS to evaluate the candidate designs and particle swarm optimization (PSO) to drive the optimization. RTHO is well-suited to evaluate nonlinear experimental substructures, in particular those that do not undergo permanent damage such as structural control devices. Structural damage, if of interest, can be modeled through the numerical component. This paper proposes and demonstrates the integration of state-of-the-art optimization algorithms with state-of-the-art experimental methods – a cyber-physical approach to structural optimization.