非线性钢抗弯框架结构地震响应预测的物理信息机器学习
Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures
AI总结：
本文提出一种物理信息机器学习（PiML）方法，结合模型降阶、小波分析、LSTM网络与牛顿第二定律，将物理约束融入深度神经网络，以稀疏数据准确预测非线性钢抗弯框架结构的地震响应，提升了模型的准确性、可解释性和鲁棒性。
摘要：
由于传统仿真的计算成本高昂，使用机器学习（ML）方法进行结构元建模引起了日益浓厚的兴趣。纯数据驱动策略在模型鲁棒性、可解释性和对大量数据的依赖性方面常面临局限。为应对这些挑战，本文引入了一种新颖的物理信息机器学习（PiML）方法，将科学原理和物理定律整合到深度神经网络中，以对非线性结构的地震响应进行建模。该方法通过三个主要特征将ML模型的解空间约束在已知物理边界内：结合模型降阶和小波分析的降维、长短期记忆（LSTM）网络以及牛顿第二定律。降维处理了结构系统的冗余并提高了效率，同时通过小波分析提取基本特征。LSTM网络捕捉时间依赖性以实现准确的时间序列预测。操纵运动方程有助于学习系统非线性，并将解限制在物理上可解释的结果内。这些属性允许在稀疏数据下进行模型训练，增强了准确性、可解释性和鲁棒性。此外，考虑了DesignSafe-CI数据库[1]中可用的在地震载荷下的原型钢抗弯框架数据集进行评估。所得元模型比现有的物理引导LSTM模型更好地处理复杂数据，并优于其他非物理数据驱动网络。
英文摘要：
There is growing interest in using machine learning (ML) methods for structural metamodeling due to the substantial computational cost of traditional simulations. Purely data-driven strategies often face limitations in model robustness, interpretability, and dependency on extensive data. To address these challenges, this paper introduces a novel physics-informed machine learning (PiML) method that integrates scientific principles and physical laws into deep neural networks to model seismic responses of nonlinear structures. The approach constrains the ML model's solution space within known physical bounds through three main features: dimensionality reduction via combined model order reduction and wavelet analysis, long short-term memory (LSTM) networks, and Newton's second law. Dimensionality reduction addresses structural systems' redundancy and boosts efficiency while extracting essential features through wavelet analysis. LSTM networks capture temporal dependencies for accurate time-series predictions. Manipulating the equation of motion helps learn system nonlinearities and confines solutions within physically interpretable results. These attributes allow for model training with sparse data, enhancing accuracy, interpretability, and robustness. Furthermore, a dataset of archetype steel moment resistant frames under seismic loading, available in the DesignSafe-CI Database [1], is considered for evaluation. The resulting metamodel handles complex data better than existing physics-guided LSTM models and outperforms other non-physics data-driven networks.
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