Seismic Structural Response and Loss Estimation for Dense Urban Districts Using Neural Network Parameterized Gaussian Process Article Swipe
Byeongseong Choi
,
Sang-ri Yi
,
Taeyong Kim
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/eqe.70087
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/eqe.70087
Earthquakes pose a major threat to urban areas, causing fatalities, injuries, and significant economic losses. This study proposes a Gaussian process parametrized by deep neural networks (DNN–GP) as an efficient surrogate for assessing seismic losses of building structures at a regional scale. The framework constructs a GP surrogate using three DNN models that predict the mean, standard deviation, and correlation of structural responses. These models are trained on a seismic demand database developed from a large number of dynamic analyses of structural systems, composed of nonlinear single‐degree‐of‐freedom systems subjected to ground motions with various scaling factors. The DNN–GP model targets assessing an aggregated loss over a cluster of buildings within a limited spatial domain that are subjected to identical ground motion. A key feature of the proposed method is its ability to refine initial estimates from the component DNN models by incorporating reference points, localized nonlinear responses obtained from a selected subset of structures within the considered building portfolio. This study demonstrates the effectiveness of the surrogate model through two numerical studies: (1) prediction of seismic responses for a set of structural systems, and (2) estimation of seismic losses for a building cluster. The results show that the DNN–GP significantly reduces the need for exhaustive nonlinear simulations while maintaining accuracy and quantifying prediction uncertainty. This enables rapid, simulation‐informed decision‐making using the surrogate model for managing seismic risk and enhancing the resilience of urban infrastructure.
Related Topics
Concepts
Parameterized complexity
Gaussian process
Surrogate model
Nonlinear system
Kriging
Artificial neural network
Computer science
Process (computing)
Gaussian
Seismic risk
Set (abstract data type)
Data mining
Resilience (materials science)
Algorithm
Component (thermodynamics)
Feature (linguistics)
Variance (accounting)
Key (lock)
Mathematical optimization
Earthquake engineering
Uncertainty quantification
Incremental Dynamic Analysis
Estimation
Surrogate data
Artificial intelligence
Spatial correlation
Basis (linear algebra)
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/eqe.70087
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/eqe.70087
- OA Status
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- References
- 36
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W7105829805Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/eqe.70087Digital Object Identifier
- Title
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Seismic Structural Response and Loss Estimation for Dense Urban Districts Using Neural Network Parameterized Gaussian ProcessWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-11-17Full publication date if available
- Authors
-
Byeongseong Choi, Sang-ri Yi, Taeyong KimList of authors in order
- Landing page
-
https://doi.org/10.1002/eqe.70087Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/eqe.70087Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/eqe.70087Direct OA link when available
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Parameterized complexity, Gaussian process, Surrogate model, Nonlinear system, Kriging, Artificial neural network, Computer science, Process (computing), Gaussian, Seismic risk, Set (abstract data type), Data mining, Resilience (materials science), Algorithm, Component (thermodynamics), Feature (linguistics), Variance (accounting), Key (lock), Mathematical optimization, Earthquake engineering, Uncertainty quantification, Incremental Dynamic Analysis, Estimation, Surrogate data, Artificial intelligence, Spatial correlation, Basis (linear algebra), EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| primary_location.license | cc-by-nc |
| primary_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/eqe.70087 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Earthquake Engineering & Structural Dynamics |
| primary_location.landing_page_url | https://doi.org/10.1002/eqe.70087 |
| publication_date | 2025-11-17 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2094721688, https://openalex.org/W2103755052, https://openalex.org/W1970082723, https://openalex.org/W2897839592, https://openalex.org/W4310259063, https://openalex.org/W4401131718, https://openalex.org/W3128222624, https://openalex.org/W3110592337, https://openalex.org/W1983136214, https://openalex.org/W4396831683, https://openalex.org/W4406666541, https://openalex.org/W4283707319, https://openalex.org/W4393295083, https://openalex.org/W2981472239, https://openalex.org/W3008799350, https://openalex.org/W3093739175, https://openalex.org/W4415513425, https://openalex.org/W1973201444, https://openalex.org/W2101555172, https://openalex.org/W4302028798, https://openalex.org/W4317214206, https://openalex.org/W2772560240, https://openalex.org/W3035732938, https://openalex.org/W2095046766, https://openalex.org/W2903856671, https://openalex.org/W4226036103, https://openalex.org/W1994005439, https://openalex.org/W2067819208, https://openalex.org/W4286781802, https://openalex.org/W2119233169, https://openalex.org/W2025207305, https://openalex.org/W3102762050, https://openalex.org/W2076739712, https://openalex.org/W3125499280, https://openalex.org/W1482440917, https://openalex.org/W2150559858 |
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