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Surrogate Structure-Specific Probabilistic Dynamic Responses of Bridge Portfolios using Deep Learning with Partial Information
Computational Engineering, Finance, and Science · Submitted 1 Mar 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
Predicting region-wide structural responses under seismic shaking is essential for enhancing the effectiveness of earthquake engineering task forces 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. To address this gap, this study designed a novel 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Predicting region-wide structural responses under seismic shaking is essential for enhancing the effectiveness of earthquake engineering task forces such as earthquake early warning and regional seismic risk and resilience assessments. | background 1.00 | 0.05 | 0.03 | asserted 1.00 |
| s1 | 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. | gap 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s2 | To address this gap, this study designed a novel 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. | contribution 0.64 | 0.94 | 0.09 | asserted 0.98 |
| s3 | 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. | result 0.94 | 0.96 | 0.20 | asserted 1.00 |
| s4 | 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. | interpretation 0.97 | 0.85 | 0.76 | asserted 0.86 |
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The paper
Original abstract
Predicting region-wide structural responses under seismic shaking is essential for enhancing the effectiveness of earthquake engineering task forces 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. To address this gap, this study designed a novel 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.
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