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References from Data-driven nonlinear seismic response prediction method for building structures by synthesizing structural metadata and seismic characteristics. Local targets link to admitted publications; unresolved targets remain external evidence.
Hazus estimated annualized earthquake losses for the United States
2017 · External reference
Moment magnitudes of two large Turkish earthquakes on February 6, 2023, from long-period coda
10.1016/j.eqs.2023.02.008 · 2023 · External reference
Field reconnaissance and observations from the February 6, 2023, Turkey earthquake sequence
10.1007/s11069-023-06143-2 · 2023 · External reference
Rupture process and aftershock focal mechanisms of the 2022 M6.8 Luding earthquake in Sichuan
10.1016/j.eqs.2022.12.005 · 2022 · External reference
An acceleration record set for different frequency content, amplitude, and site classes
2019 · External reference
Machine learning algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones
10.1016/j.istruc.2024.107639 · 2024 · External 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 · 2023 · External reference
Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frame
2021 · External reference
Rapid seismic damage state assessment of RC frames using machine learning methods
2023 · External reference
Machine learning for risk and resilience assessment in structural engineering: Progress and future trends
10.1061/(asce)st.1943-541x.0003392 · 2022 · External reference
Prediction of damage potential in mainshock-aftershock sequences using machine learning algorithms
10.1007/s11803-024-2280-6 · 2024 · External reference
Structural damage prediction of a reinforced concrete frame under single and multiple seismic events using machine learning algorithms
10.3390/app12083845 · 2022 · External reference
Unresolved reference
1970 · External reference
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 · External reference
Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning
10.1080/13632469.2020.1826371 · 2022 · External 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 · 2024 · External reference
Time-frequency feature-based seismic response prediction neural network model for building structures
10.3390/app13052956 · 2023 · External reference
Structural response prediction for damage identification using wavelet spectra in convolutional neural network
10.3390/s21206795 · 2021 · External reference
Investigation on employment of time and frequency domain data for predicting nonlinear seismic responses of structures
10.1016/j.istruc.2024.105996 · 2024 · External reference
Seismic response prediction of a damped structure based on data-driven machine learning methods
10.1016/j.engstruct.2023.117264 · 2024 · External reference
Neural networks for the rapid seismic assessment of existing moment-frame RC buildings
10.1016/j.ijdrr.2021.102677 · 2022 · External reference
Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
10.1016/j.engappai.2023.107388 · 2024 · External reference
Quantum-enhanced machine learning technique for rapid post-earthquake assessment of building safety, Computer-Aided
10.1111/mice.13291 · 2024 · External reference
Machine learning-based fast seismic risk assessment of building structures
10.1080/13632469.2021.1987354 · 2022 · External 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 · 2024 · External reference
Enhancing seismic performance prediction of RC frames using MFF-ANN model approach
10.1007/s11042-023-16931-4 · 2024 · External reference
Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data
10.1038/s41598-024-81705-3 · 2024 · External 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 · 2025 · External reference
Data-driven machine-learning-based seismic response prediction and damage classification for an unreinforced masonry building
10.3390/app15041686 · 2025 · External reference
Regional-scale nonlinear structural seismic response prediction by neural network
10.1016/j.engfailanal.2023.107707 · 2023 · External reference
Rapid seismic response prediction of RC frames based on deep learning and limited building information
10.1016/j.engstruct.2022.114638 · 2022 · External reference
Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
10.1016/j.cma.2025.118079 · 2025 · External reference
Physics-informed long short-term memory network with data folding for efficient site seismic response prediction
10.1016/j.cacaie.2026.100054 · 2026 · External 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 · 2026 · External reference
Unresolved reference
2014 · External reference
Rapid decision-making tool of pilot-type RC building structure for seismic performance evaluation and retrofit strategy using multi-dimensional structural parameter surface
2021 · External reference
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 · External reference
Unresolved reference
External reference
Unresolved reference
2009 · External reference
Unresolved reference
External reference
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 · ExternalCitation · 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 · ExternalCitation · doi-reference
Enhancing seismic performance prediction of RC frames using MFF-ANN model approach
10.1007/s11042-023-16931-4 · ExternalCitation · doi-reference
Field reconnaissance and observations from the February 6, 2023, Turkey earthquake sequence
10.1007/s11069-023-06143-2 · ExternalCitation · doi-reference
Prediction of damage potential in mainshock-aftershock sequences using machine learning algorithms
10.1007/s11803-024-2280-6 · ExternalCitation · doi-reference
Physics-informed long short-term memory network with data folding for efficient site seismic response prediction
10.1016/j.cacaie.2026.100054 · ExternalCitation · doi-reference
Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction
10.1016/j.cma.2025.118079 · ExternalCitation · doi-reference
Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
10.1016/j.engappai.2023.107388 · ExternalCitation · doi-reference
Regional-scale nonlinear structural seismic response prediction by neural network
10.1016/j.engfailanal.2023.107707 · ExternalCitation · doi-reference
Rapid seismic response prediction of RC frames based on deep learning and limited building information
10.1016/j.engstruct.2022.114638 · ExternalCitation · doi-reference
Seismic response prediction of a damped structure based on data-driven machine learning methods
10.1016/j.engstruct.2023.117264 · ExternalCitation · doi-reference
Rupture process and aftershock focal mechanisms of the 2022 M6.8 Luding earthquake in Sichuan
10.1016/j.eqs.2022.12.005 · ExternalCitation · doi-reference
Moment magnitudes of two large Turkish earthquakes on February 6, 2023, from long-period coda
10.1016/j.eqs.2023.02.008 · ExternalCitation · doi-reference
Neural networks for the rapid seismic assessment of existing moment-frame RC buildings
10.1016/j.ijdrr.2021.102677 · ExternalCitation · doi-reference
Investigation on employment of time and frequency domain data for predicting nonlinear seismic responses of structures
10.1016/j.istruc.2024.105996 · ExternalCitation · doi-reference
Machine learning algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones
10.1016/j.istruc.2024.107639 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · doi-reference
Machine learning for risk and resilience assessment in structural engineering: Progress and future trends
10.1061/(asce)st.1943-541x.0003392 · ExternalCitation · doi-reference
Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning
10.1080/13632469.2020.1826371 · ExternalCitation · doi-reference
Machine learning-based fast seismic risk assessment of building structures
10.1080/13632469.2021.1987354 · ExternalCitation · doi-reference
Quantum-enhanced machine learning technique for rapid post-earthquake assessment of building safety, Computer-Aided
10.1111/mice.13291 · ExternalCitation · doi-reference
Structural damage prediction of a reinforced concrete frame under single and multiple seismic events using machine learning algorithms
10.3390/app12083845 · ExternalCitation · doi-reference
Time-frequency feature-based seismic response prediction neural network model for building structures
10.3390/app13052956 · ExternalCitation · doi-reference
Data-driven machine-learning-based seismic response prediction and damage classification for an unreinforced masonry building
10.3390/app15041686 · ExternalCitation · doi-reference
Structural response prediction for damage identification using wavelet spectra in convolutional neural network
10.3390/s21206795 · ExternalCitation · 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 · ExternalCitation · doi-reference
