基于机器学习的结构支撑非线性地震响应预测
Predicting Nonlinear Seismic Response of Structural Braces Using Machine Learning
摘要：
各种具有高度非线性行为的结构材料的数值模拟一直是工程学科中具有挑战性的问题。实验数据通常用于表征这种行为。本研究旨在通过使用最先进的机器学习技术来提高建模能力，并试图回答几个科学问题：（i）哪种ML算法能够更有效地学习这样一个复杂的非线性问题？(ii)是否有可能人工再现结构支撑的抗震性能，可以代表真正的物理？(iii)我们的发现如何扩展到由类似的非线性动力学驱动的不同工程问题？为了回答这些问题，所提出的方法进行了验证，使用实验支撑数据。本文表明，经过适当的数据准备，长短期记忆（LSTM）方法是高度能够捕捉支撑的非线性行为。此外，调整超参数的模型，如层数，神经元数量，和激活函数的影响，提出。最后，简要讨论了利用深度神经网络算法学习非线性动力学的能力及其优势。
英文摘要：
Numerical modeling of different structural materials that have highly nonlinear behaviors has always been a challenging problem in engineering disciplines. Experimental data is commonly used to characterize this behavior. This study aims to improve the modeling capabilities by using state of the art Machine Learning techniques, and attempts to answer several scientific questions: (i) Which ML algorithm is capable and is more efficient to learn such a complex and nonlinear problem? (ii) Is it possible to artificially reproduce structural brace seismic behavior that can represent real physics? (iii) How can our findings be extended to the different engineering problems that are driven by similar nonlinear dynamics? To answer these questions, the presented methods are validated by using experimental brace data. The paper shows that after proper data preparation, the long-short term memory (LSTM) method is highly capable of capturing the nonlinear behavior of braces. Additionally, the effects of tuning the hyperparameters on the models, such as layer numbers, neuron numbers, and the activation functions, are presented. Finally, the ability to learn nonlinear dynamics by using deep neural network algorithms and their advantages are briefly discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
