水准点(测量)
人工神经网络
计算机科学
阻尼器
理论(学习稳定性)
近似误差
计算机模拟
比例(比率)
迭代法
算法
控制理论(社会学)
模拟
结构工程
人工智能
工程类
机器学习
物理
控制(管理)
大地测量学
量子力学
地理
作者
Fukang Gao,Zhenyun Tang,Xiuli Du
出处
期刊:Earthquake engineering and resilience
日期:2023-12-26
摘要
Abstract 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%.
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