Physics-Reinforced Deep Learning for Structural Metamodeling

这项研究将开发新的计算方法，以推进地震作用下的民用结构体系的建模、分析和评估。目前在结构分析中广泛使用的简化或降阶模型在准确预测结构在地震荷载作用下(如存在大的非线性漂移、损伤、破坏等)的复杂非线性响应方面存在严重的局限性。人工智能(AI)在土木工程中的应用为结构建模提供了一种强有力的新途径。然而，目前训练一个可靠的人工智能模型需要大量的数据，即使有丰富的数据，训练的模型也很难解释，几乎没有物理意义。为了解决这些基本问题，并弥合人工智能和基于性能的工程之间的知识差距，该项目将整合深度学习和物理原理，以便在地震危险下对非线性结构进行高效和概率建模。这项研究将以更少的计算量推动工程结构更高效的设计、可靠性分析、控制和优化。该项目还将建立一个综合的研究-教育-推广计划，该计划将(I)转变基于特定领域知识的对机器学习的基本理解，(Ii)促进本科生，特别是女性和少数族裔的参与，以及(Iii)激励高中生从事与STEM相关的职业。这项研究将把机器学习的新元素注入基于性能的结构工程，通过利用深度学习与物理知识相结合的方式为结构系统的地震响应建模开辟一条新的途径。该项目的具体研究目标包括：(1)开发基于无监督学习的地面运动选择以优化地震响应数据库的生成；(2)建立用于结构元建模的创新的、物理强化的深度学习范式的严格公式和算法；(3)开发神经网络压缩方法以剪裁元模型以进行高效推理；以及(4)将变异性纳入概率地震响应预测和易损性分析。由此产生的基于深度学习的元模型将具有显著特征，包括(I)具有物理意义的可解释性，(Ii)对未知情况的泛化和外推，(Iii)基于轻量级/压缩的元模型体系结构的实时推理，以及(Iv)处理不完整和稀缺数据的能力。该方法适用于复杂载荷条件下的多种非线性动力系统。这个项目将推进非线性结构动力学、计算建模、稳健和高效的机器/深度学习、优化和不确定性量化等多个学科的知识库。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research will develop new computational methods to advance modeling, analysis and assessment of civil structural systems subjected to earthquakes. Currently available simplified or reduced-order models widely used in structural analysis have severe limitations in accurately predicting the complex nonlinearities in structural response under earthquake loading (e.g., in the presence of large nonlinear drifts, damage, failure, etc.). The infusion of artificial intelligence (AI) into civil engineering offers a powerful new approach for structural modeling. However, currently large amounts of data are required to train a reliable AI model, and even with rich data, the trained models are difficult to interpret and have little physical meaning. To address these fundamental issues and to bridge the knowledge gap between AI and performance-based engineering, this project will integrate deep learning and physics principles for efficient and probabilistic modeling of nonlinear structures under earthquake hazards. This research will advance more efficient design, reliability analysis, control and optimization of engineering structures with much less computational efforts. The project will also establish an integrated research-education-outreach program that will (i) transform the fundamental understanding of machine learning grounded with domain-specific knowledge, (ii) promote participation by undergraduates, in particular, women and minorities, and (iii) inspire high school students to pursue STEM-related careers.This research will breathe novel elements of machine learning into performance-based structural engineering, opening a new avenue by leveraging deep learning integrated with physics knowledge for modeling of seismic response of structural systems. The specific research aims of this project include: (1) developing unsupervised learning-based ground motion selection for optimal generation of seismic response database, (2) establishing rigorous formulation and algorithm for an innovative, physics-reinforced deep Learning paradigm for structural metamodeling, (3) developing a neural network compression approach to prune the metamodels for efficient inference, and (4) incorporating variability for probabilistic seismic response prediction and fragility analysis. The resulting deep-learning-based metamodels will possess salient features including (i) interpretability with physical meaning, (ii) generalizability and extrapolation to unseen cases, (iii) real-time inference based on a lightweight/compressed metamodel architecture, and (iv) capability of addressing incomplete and scare data. This approach will be applicable to a wide range of nonlinear dynamical systems under complex loading conditions. This project will advance the knowledge base in multiple disciplines of nonlinear structural dynamics, computational modeling, robust and efficient machine/deep learning, optimization and uncertainty quantification.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.