Implementation of offline iterative hybrid simulation based on neural networks

Real-time hybrid simulation (RTHS) is a testing method that combines numerical simulation and physical testing, enabling large-scale or even full-scale tests of large and complex engineering structures using the existing experimental facilities. At present, the accuracy and stability of RTHS are limited by the loading device and numerical solution efficiency. The experimental method was improved based on the neural network, and an off-line iterative hybrid simulation method based on neural networks was implemented. Taking the tuned damping structure as examples, the dynamic metamodels of the tuned mass damper and tuned liquid damper were established based on the long-short time memory (LSTM) neural network, and the model was iteratively simulated globally with the 9-story benchmark model to calculate the response of the damping structure. The error of damper reaction predicted by LSTM neural network model is within 5.16%. The global iterative method can converge after a limited number of iterations, and the peak error of the structural response is within 7.85%. In this paper, the real-time hybrid simulation method was improved based on neural networks, and an offline iterative hybrid simulation method based on neural networks was implemented. Taking the tuned damping structure as examples, the dynamic metamodels of the tuned mass damper and tuned liquid damper were established based on the long-short time memory neural network, and the model was iteratively simulated globally with the 9-story benchmark model to calculate the response of the damping structure. image