Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling

By Ruiyang Zhang, Yang Liu. 2019

460 citations recorded by the source corpus.

Seismic events, among many other natural hazards, reduce due functionality and exacerbate vulnerability of in-service buildings. Accurate modeling and prediction of building's response subjected to earthquakes makes possible to evaluate building performance. To this end, we leverage the recent advances in deep learning and develop a physics-guided convolutional neural network (PhyCNN) framework for data-driven seismic response modeling and serviceability assessment of buildings. The proposed PhyCNN approach is capable of accurately predicting building's seismic response in a data-driven fashion without the need of a physics-based analytical/numerical model. The basic concept is to train a deep PhyCNN model based on available seismic input-output datasets (e.g., from simulation or sensing) and physics constraints. The trained PhyCNN can then used as a surrogate model for structural seismic response prediction. Available physics (e.g., the law of dynamics) can provide constraints to the network outputs, alleviate overfitting issues, reduce the need of big training datasets, and thus improve the robustness of the trained model for more reliable prediction. The trained surrogate model is then utilized for fragility analysis given certain limit state criteria (e.g., the serviceability state). In addition, an unsupervised learning algorithm based on K-means clustering is also proposed to partition the limited number of datasets to training, validation and prediction categories, so as to maximize the use of limited datasets. The performance of the proposed approach is demonstrated through three case studies including both numerical and experimental examples. Convincing results illustrate that the proposed PhyCNN paradigm outperforms conventional pure data-based neural networks.

Subfields: Civil and Structural Engineering

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Citations in the corpus

Physics informed deep learning for computational elastodynamics without labeled data (2020, background): “uations [10,17{21], solving stochastic PDEs [22], ows transport in porous media [23{25], cardiovascular systems [26{28], design of metamaterials [29{ 31], metamodeling of nonlinear structural systems [32,33], and discovery of physical laws [34{36], among others. To further improve the learning perfo”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “…information, readers are recommended to read the recent comprehensive survey about integrating physics-based modeling with ML as presented by Willard et al. 341 In the study by Zhang et al., 234 the authors proposed a physics-guided DL surrogate model for seismic structural response prediction.”
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (2020, background): “However, when the governing PDEs are known, their solutions can be learned in a physics-constrained fashion with less data [2, 13, 14] or even without any data [3, 4, 15].”
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems (2021, methodology): “Zhang et al. [22] presented a physics-guided CNN for seismic response modeling and also explored the similar idea with a Recurrent Neural Network (RNN) for metamodeling of nonlinear structures [23].”