  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.
