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.
