BRITE Relaunch: Improving Structural Health by Advancing Interpretable Machine Learning for Nonlinear Dynamics

这个促进工程变革和公平进步的研究思路（BRITE）重新启动奖将专注于推进可解释的机器学习，以使建模准确性与透明度相匹配。这将为结构工程师提供一个上级和值得信赖的工具来模拟非线性动力系统。对结构和材料在各种动力荷载作用下的复杂行为进行建模，对于智能结构、结构控制、非线性系统识别、损伤检测和地震工程来说，仍然是一个重大挑战。机器学习正在成为应对这一挑战的流行方法。然而，这些系统的物理知识，这些系统的工程实践设计和机器学习方法产生的模型之间存在着巨大的差距。机器学习缺乏可解释性和透明度。该研究项目将根据工程师的知识和培训，物理信息，并赋予工程师的判断力，开发系统的解决方案。该研究在改善基础设施健康、减轻地震、风灾害和气候变化的后果方面直接造福社会。该研究项目建立在项目负责人过去的工作基础上，以推进“使用可解释机器学习的非线性静态函数近似”，给出了它在近似非线性本构关系中的直接使用以及它在近似常微分方程中的非线性被积函数中的使用，非线性动力学为了实现可解释和物理信息的机器学习方法，该研究项目将创建新的算法和实现程序。利用高等应用数学中的神经流形理论，使S型神经网络的训练过程具有可解释性。将利用图论来创建知识图，以便在初始化过程中自动使用可解释的机器学习来近似非线性静态函数，以近似非线性静态函数，并可用于深度学习。除了广泛的交叉验证，该项目的方法的主要应用将通过使用数字孪生环境中的真实世界数据进行研究，这是结构工程中最先进的系统级建模框架。此外，还将使用木梁柱节点进行全面的实验室演示和验证，以激发人们对结构工程中非线性动力学广泛相关性的广泛兴趣。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Relaunch award will focus on advancing interpretable machine learning to match modeling accuracy with transparency. This will provide structural engineers with a superior and trustworthy tool to model nonlinear dynamical systems. Modeling the complex behaviors of structures and materials under various types of dynamic loads remains a major challenge for smart structures, structural control, nonlinear system identification, damage detection, and earthquake engineering. Machine learning is becoming a popular approach for meeting this challenge. However, there is a significant gap between knowledge of the physics of these systems, the engineering practice design of these systems, and the models produced from machine learning methods. Machine learning lacks interpretability and transparency. This research project will develop systematic solutions with reasoning based on the engineers’ knowledge and training, physics-informed, and empowering the engineers’ judgment. This research directly benefits society in terms of improving infrastructure health, mitigating the consequences of earthquake, wind hazards, and climate change.This research project builds upon the project leader’s past work to advance “nonlinear static function approximation using interpretable machine learning”, given its direct use in approximating nonlinear constitutive relations and its use in approximating nonlinear integrands in ordinary differential equations for nonlinear dynamics. To achieve interpretable and physics-informed machine learning methods, this research project will create new algorithms and implementation procedures. Neuromanifold theories in advanced applied mathematics will be employed to make the training process of sigmoidal neural networks interpretable. Graph theory will be leveraged to create knowledge graphs so that nonlinear static function approximation using interpretable machine learning can be automated during initialization to approximate nonlinear static functions and can be used for deep learning. In addition to extensive cross-validations, a major application of the project's approach will be investigated by using real-world data in a digital twin setting, the state-of-the-art system-level modeling framework in structural engineering. Also, a comprehensive laboratory demonstration and validation will be carried out using timber beam-column joints to generate broad interest in the broad relevance of nonlinear dynamics in structural engineering.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.