Abstract
No public abstract is available from the verified source records.
Insub Choi, Han Yong Lee, Byung Kwan Oh
Abstract
Authors
Institutions
Provenance
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Hazus estimated annualized earthquake losses for the United States
2017
Moment magnitudes of two large Turkish earthquakes on February 6, 2023, from long-period coda
10.1016/j.eqs.2023.02.008 · 2023
Field reconnaissance and observations from the February 6, 2023, Turkey earthquake sequence
10.1007/s11069-023-06143-2 · 2023
Rupture process and aftershock focal mechanisms of the 2022 M6.8 Luding earthquake in Sichuan
10.1016/j.eqs.2022.12.005 · 2022
An acceleration record set for different frequency content, amplitude, and site classes
2019
Machine learning algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones
10.1016/j.istruc.2024.107639 · 2024
The effect of soil-structure interaction on the seismic response of structures using machine learning, finite element modeling, and ASCE 7-16 methods
10.3390/s23042047 · 2023
Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frame
2021
Rapid seismic damage state assessment of RC frames using machine learning methods
2023
Machine learning for risk and resilience assessment in structural engineering: Progress and future trends
10.1061/(asce)st.1943-541x.0003392 · 2022
Prediction of damage potential in mainshock-aftershock sequences using machine learning algorithms
10.1007/s11803-024-2280-6 · 2024
Structural damage prediction of a reinforced concrete frame under single and multiple seismic events using machine learning algorithms
10.3390/app12083845 · 2022
Unresolved referenced work
1970
Comparative study on filter and wrapper methods for selecting ground motion intensity measures in machine learning-based seismic damage assessment of urban reinforced concrete frame structures
2025
Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning
10.1080/13632469.2020.1826371 · 2022
Rapid seismic-damage assessment method for buildings on a regional scale based on spectrum-compatible data augmentation and deep learning
10.1016/j.soildyn.2024.108504 · 2024
Time-frequency feature-based seismic response prediction neural network model for building structures
10.3390/app13052956 · 2023
Structural response prediction for damage identification using wavelet spectra in convolutional neural network
10.3390/s21206795 · 2021
Investigation on employment of time and frequency domain data for predicting nonlinear seismic responses of structures
10.1016/j.istruc.2024.105996 · 2024
Seismic response prediction of a damped structure based on data-driven machine learning methods
10.1016/j.engstruct.2023.117264 · 2024
Neural networks for the rapid seismic assessment of existing moment-frame RC buildings
10.1016/j.ijdrr.2021.102677 · 2022
Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
10.1016/j.engappai.2023.107388 · 2024
Quantum-enhanced machine learning technique for rapid post-earthquake assessment of building safety, Computer-Aided
10.1111/mice.13291 · 2024
Machine learning-based fast seismic risk assessment of building structures
10.1080/13632469.2021.1987354 · 2022
Convolutional neural network-based seismic response prediction method using spectral acceleration of earthquakes and conditional vector of structural property
10.1016/j.soildyn.2024.109021 · 2024
Enhancing seismic performance prediction of RC frames using MFF-ANN model approach
10.1007/s11042-023-16931-4 · 2024
Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data
10.1038/s41598-024-81705-3 · 2024
Machine learning-based processes with active learning strategies for the automatic rapid assessment of seismic resistance of steel frames
10.1016/j.istruc.2025.108227 · 2025
Data-driven machine-learning-based seismic response prediction and damage classification for an unreinforced masonry building
10.3390/app15041686 · 2025
Regional-scale nonlinear structural seismic response prediction by neural network
10.1016/j.engfailanal.2023.107707 · 2023
Rapid seismic response prediction of RC frames based on deep learning and limited building information
10.1016/j.engstruct.2022.114638 · 2022
Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
10.1016/j.cma.2025.118079 · 2025
Physics-informed long short-term memory network with data folding for efficient site seismic response prediction
10.1016/j.cacaie.2026.100054 · 2026
A novel Fourier feature physics-informed neural networks based on the boundary element method for solving scattering of SH wave induced by complex topography
10.1002/eqe.70162 · 2026
Unresolved referenced work
2014
Rapid decision-making tool of pilot-type RC building structure for seismic performance evaluation and retrofit strategy using multi-dimensional structural parameter surface
2021
The N2 method for the seismic damage analysis of RC buildings
10.1002/(sici)1096-9845(199601)25:1<31::aid-eqe534>3.0.co;2-v · 1996
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
2009
Unresolved referenced work
Kept as external metadata until matched
The N2 method for the seismic damage analysis of RC buildings
10.1002/(sici)1096-9845(199601)25:1<31::aid-eqe534>3.0.co;2-v · doi-reference
A novel Fourier feature physics-informed neural networks based on the boundary element method for solving scattering of SH wave induced by complex topography
10.1002/eqe.70162 · doi-reference
Physics-informed long short-term memory network with data folding for efficient site seismic response prediction
10.1016/j.cacaie.2026.100054 · doi-reference
Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
10.1016/j.cma.2025.118079 · doi-reference
Rapid seismic response prediction of RC frames based on deep learning and limited building information
10.1016/j.engstruct.2022.114638 · doi-reference
Regional-scale nonlinear structural seismic response prediction by neural network
10.1016/j.engfailanal.2023.107707 · doi-reference
Data-driven machine-learning-based seismic response prediction and damage classification for an unreinforced masonry building
10.3390/app15041686 · doi-reference
Machine learning-based processes with active learning strategies for the automatic rapid assessment of seismic resistance of steel frames
10.1016/j.istruc.2025.108227 · doi-reference
Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data
10.1038/s41598-024-81705-3 · doi-reference
Enhancing seismic performance prediction of RC frames using MFF-ANN model approach
10.1007/s11042-023-16931-4 · doi-reference
Convolutional neural network-based seismic response prediction method using spectral acceleration of earthquakes and conditional vector of structural property
10.1016/j.soildyn.2024.109021 · doi-reference
Machine learning-based fast seismic risk assessment of building structures
10.1080/13632469.2021.1987354 · doi-reference
Quantum-enhanced machine learning technique for rapid post-earthquake assessment of building safety, Computer-Aided
10.1111/mice.13291 · doi-reference
Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
10.1016/j.engappai.2023.107388 · doi-reference
Neural networks for the rapid seismic assessment of existing moment-frame RC buildings
10.1016/j.ijdrr.2021.102677 · doi-reference
Seismic response prediction of a damped structure based on data-driven machine learning methods
10.1016/j.engstruct.2023.117264 · doi-reference
Investigation on employment of time and frequency domain data for predicting nonlinear seismic responses of structures
10.1016/j.istruc.2024.105996 · doi-reference
Structural response prediction for damage identification using wavelet spectra in convolutional neural network
10.3390/s21206795 · doi-reference
Time-frequency feature-based seismic response prediction neural network model for building structures
10.3390/app13052956 · doi-reference
Rapid seismic-damage assessment method for buildings on a regional scale based on spectrum-compatible data augmentation and deep learning
10.1016/j.soildyn.2024.108504 · doi-reference
Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning
10.1080/13632469.2020.1826371 · doi-reference
Structural damage prediction of a reinforced concrete frame under single and multiple seismic events using machine learning algorithms
10.3390/app12083845 · doi-reference
Prediction of damage potential in mainshock-aftershock sequences using machine learning algorithms
10.1007/s11803-024-2280-6 · doi-reference
Machine learning for risk and resilience assessment in structural engineering: Progress and future trends
10.1061/(asce)st.1943-541x.0003392 · doi-reference
The effect of soil-structure interaction on the seismic response of structures using machine learning, finite element modeling, and ASCE 7-16 methods
10.3390/s23042047 · doi-reference
Machine learning algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones
10.1016/j.istruc.2024.107639 · doi-reference
Rupture process and aftershock focal mechanisms of the 2022 M6.8 Luding earthquake in Sichuan
10.1016/j.eqs.2022.12.005 · doi-reference
Field reconnaissance and observations from the February 6, 2023, Turkey earthquake sequence
10.1007/s11069-023-06143-2 · doi-reference
Moment magnitudes of two large Turkish earthquakes on February 6, 2023, from long-period coda
10.1016/j.eqs.2023.02.008 · doi-reference