Surrogate structure-specific probabilistic dynamic responses of bridge portfolios using deep learning with partial information

Predicting region-wide structural responses under seismic shaking is essential for enhancing the effectiveness of earthquake engineering tasks such as earthquake early warning and regional seismic risk and resilience assessments. Existing domain-specific and data-driven approaches, however, lack the capability to provide high-fidelity, structure-specific dynamic response predictions for large-scale structural inventories in a timely manner, especially when structural parameters and detailing are incomplete or unavailable. To address this gap, this study developed a deep learning framework, which integrates heterogeneous ground motion sequences and partial structural information as model inputs, to predict structure-specific, probabilistic dynamic responses of regional structural portfolios. Validation on a portfolio of highway bridges in California demonstrates the model’s ability to capture inter-structure response variability by inputting critical and accessible bridge parameters while accounting for uncertainties due to the lack of other information. The results underscore the framework’s efficiency and accuracy, paving the way for various advancements in performance-based earthquake engineering and regional-scale seismic decision-making.