Communication Development and Verification for Python-Based Machine Learning Models for Real-Time Hybrid Simulation

Abstract

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 the different disciplines keep evolving and likewise, is 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 Python-based advanced ML models for RTHS analytical substructures. Deep long short-term memory (LSTM) networks in Python were considered for the advanced metamodeling for RTHS tests. The performance of Python can be enhanced by running the models using high-performance 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 eliminating delay compensators when ML models are used for RTHS.