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Hybrid simulation (HS) is an advanced simulation method that couples experimental testing and analytical modeling to better understand structural systems and individual components’ behavior under extreme events such as earthquakes. Conducting HS and real-time HS (RTHS) can be challenging with complex analytical substructures due to the nature of direct integration algorithms when the finite element method is employed. Thus, alternative methods such as machine learning (ML) models could help tackle these difficulties. This study aims to investigate the quality of the RTHS tests when a deep learning algorithm is used as a metamodel to represent the dynamic behavior of a nonlinear analytical substructure. The compact HS laboratory at the University of Nevada, Reno was utilized to conduct exclusive RTHS tests. Simulating a braced frame structure, the RTHS tests combined, for the first time, linear brace model specimens (physical substructure) along with nonlinear ML models for the frame (analytical substructure). Deep long short-term memory (Deep-LSTM) networks were employed and trained to develop the metamodels of the analytical substructure using the Python environment. The training dataset was obtained from pure analytical finite element simulations for the complete structure under earthquake excitation. The RTHS evaluations were first conducted for virtual RTHS tests, where substructuring was sought between the LSTM metamodel and virtual experimental substructure. To validate the proposed RTHS testing methodology and full system, several actual RTHS tests were conducted. The results from ML-based RTHS were evaluated for different ML models and compared against results from conventional RTHS with finite element models. The paper demonstrates the potential of conducting successful experimental RTHS using Deep-LSTM models, which could open the door for unparalleled new opportunities in structural systems design and assessment.

Hybrid simulation (HS) combines analytical modeling with experimental testing to provide a better understanding of both structural elements and entire systems while keeping cost-effective solutions. However, extending real-time HS (RTHS) to bigger problems becomes challenging when the analytical models get more complex. On the other hand, using machine learning (ML) techniques in solving engineering problems across different disciplines keeps evolving and likewise is a promising resource for structural engineering. The main goal of this study is to explore the validity of ML models for conducting RTHS and specifically introduce and validate the necessary communication schemes to achieve this goal. A preliminary study with a simplified linear regression ML model that can be readily implemented in Simulink is presented first to introduce the idea of using metamodels as analytical substructures. However, for ML, commonly used platforms for RTHS such as Simulink and MATLAB have limited capacity when compared to Python for instance. Thus, the main focus of this study was to introduce Pythonbased advanced ML models for RTHS analytical substructures. Deep long short-term memory networks in Python were considered for advanced metamodeling for RTHS tests. The performance of Python can be enhanced by running the models using highperformance computers, which was also considered in this study. Several RTHS tests were successfully conducted at the University of Nevada, Reno, with Python-based ML algorithms that were run from both local PC and a cluster. The tests were validated through comparisons with the pure analytical solutions obtained from finite element models. The study also explored the idea of embedding the delay compensators within the ML model for RTHS.

Hybrid simulation (HS) is a widely used structural testing method that combines a computational substructure with a numerical model for well-understood components and an experimental substructure for other parts of the structure that are physically tested. One challenge for fast HS or real-time HS (RTHS) is associated with the analytical substructures of relatively complex structures, which could have large number of degrees of freedoms (DOFs), for instance. These large DOFs computations could be hard to perform in real-time, even with the all current hardware capacities. In this study, a metamodeling technique is proposed to represent the structural dynamic behavior of the analytical substructure. A preliminary study is conducted where a one-bay one-story concentrically braced frame (CBF) is tested under earthquake loading by using a compact HS setup at the University of Nevada, Reno. The experimental setup allows for using a smallscale brace as the experimental substructure combined with a steel frame at the prototype full-scale for the analytical substructure. Two different machine learning algorithms are evaluated to provide a valid and useful metamodeling solution for analytical substructure. The metamodels are trained with the available data that is obtained from the pure analytical solution of the prototype steel frame. The two algorithms used for developing the metamodels are: (1) linear regression (LR) model, and (2) basic recurrent neural network (RNN). The metamodels are first validated against the pure analytical response of the structure. Next, RTHS experiments are conducted by using metamodels. RTHS test results using both LR and RNN models are evaluated, and the advantages and disadvantages of these models are discussed.