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References from Machine learning prediction models for ground motion parameters and seismic damage assessment of buildings at a regional scale. Local targets link to admitted publications; unresolved targets remain external evidence.
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Modeling of damage-related earthquake losses in a moderate seismic-prone country and cost–benefit evaluation of retrofit investments: application to France
10.1007/s11069-017-3061-6 · 2018 · External reference
Earthquake damage and rehabilitation intervention prediction using machine learning
2023 · External reference
Unresolved reference
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Unresolved reference
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Analytical versus observational fragilities: the case of Pettino (L'Aquila) damage data database
10.1007/s10518-014-9658-1 · 2015 · External reference
A methodology for deriving analytical fragility curves for masonry buildings based on stochastic nonlinear analyses
10.1016/j.engstruct.2010.01.009 · 2010 · External reference
Seismic vulnerability assessment of existing school buildings
10.1016/j.compstruc.2021.106522 · 2021 · External reference
Performance based grouping and fragility analysis of box-girder bridges in California
2017 · External reference
Development of seismic vulnerability assessment methodologies over the past 30 years
2006 · External reference
Seismic building damage prediction from GIS-based building data using artificial intelligence system
10.3389/fbuil.2020.576919 · 2020 · External reference
A vector-valued ground motion intensity measure consisting of spectral acceleration and epsilon
10.1002/eqe.474 · 2005 · External reference
Seismic vulnerability assessment of a historical church: limit analysis and nonlinear finite element analysis
10.1155/2013/517454 · 2013 · External reference
Seismic vulnerability and failure modes simulation of ancient masonry towers by validated virtual finite element models
10.1016/j.engfailanal.2015.07.030 · 2015 · External reference
Seismic vulnerability assessment of a monumental masonry building
10.1016/j.engstruct.2017.01.035 · 2017 · External reference
October. Seismic vulnerability assessment of confined masonry structures by macro-modeling approach
2020 · External reference
Assessment of seismic vulnerability of a historical masonry building
10.3390/buildings2030332 · 2012 · External reference
Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks
2022 · External reference
Current challenges and future trends in analytical fragility and vulnerability modeling
10.1193/042418eqs101o · 2019 · External reference
A review on application of soft computing techniques for the rapid visual safety evaluation and damage classification of existing buildings
2021 · External reference
The promise of implementing machine learning in earthquake engineering: a state-of-the-art review
10.1177/8755293020919419 · 2020 · External reference
Machine learning applications for building structural design and performance assessment: state-of-the-art review
2021 · External reference
Rapid seismic damage state assessment of RC frames using machine learning methods
2023 · External reference
Classification of buildings' potential for seismic damage using a machine learning model with auto hyperparameter tuning
10.1016/j.engstruct.2023.116359 · 2023 · External reference
Seismic damage prediction of RC buildings using machine learning
10.1002/eqe.3907 · 2023 · External reference
Machine learning-based classification for rapid seismic damage assessment of buildings at a regional scale
10.1080/13632469.2023.2252521 · 2023 · External reference
Multiclass seismic damage detection of buildings using quantum convolutional neural network
2023 · External reference
Computer-vision and machine-learning-based seismic damage assessment of reinforced concrete structures
10.3390/buildings13051258 · 2023 · External reference
Machine learning-based fast seismic risk assessment of building structures
10.1080/13632469.2021.1987354 · 2022 · External reference
Neural networks for quick earthquake damage estimation
10.1002/eqe.4290240404 · 1995 · External reference
On the application of machine learning techniques to derive seismic fragility curves
10.1016/j.compstruc.2019.03.004 · 2019 · 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
An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks
10.1016/j.engstruct.2010.03.010 · 2010 · External reference
Determining earthquake performances of existing reinforced concrete buildings by using ANN
2015 · External reference
A machine learning framework for assessing post-earthquake structural safety
10.1016/j.strusafe.2017.12.001 · 2018 · External reference
A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake
10.1177/8755293020936714 · 2020 · External reference
Classifying earthquake damage to buildings using machine learning
10.1177/8755293019878137 · 2020 · External reference
Rapid earthquake loss assessment based on machine learning and representative sampling
10.1177/87552930211042393 · 2022 · External reference
10.1016/j.cageo.2010.07.002
10.1016/j.cageo.2010.07.002 · External reference
Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake
10.1177/87552930221106495 · 2022 · External reference
Unresolved reference
2006 · External reference
Stochastic prediction of ground motion and spectral response parameters at hard-rock sites in eastern North America
1987 · External reference
Preliminary analysis for attenuation of peak ground velocity on stiff site
1993 · External reference
Attenuation characteristics of ground motion intensity from earthquakes with intermediate depth
10.1007/s10950-005-1594-8 · 2005 · External reference
Application of a new machine learning model to improve earthquake ground motion predictions
2023 · External reference
The empirical prediction of ground motion
1982 · External reference
Empirical attenuation of ground-motion spectral amplitudes in south-eastern Canada and the north-eastern United States
10.1785/0120030175 · 2004 · External reference
Ground motion prediction equation for the average horizontal component of PGA, PGV, and 5% damped acceleration response spectra at periods ranging from 0.033 to 8.0 s in southwest China
10.1016/j.soildyn.2022.107297 · 2022 · External reference
Seismic hazard analysis of Silchar city located in North East India
10.1080/19475705.2023.2170831 · 2023 · External reference
Machine learning in ground motion prediction
2021 · External reference
Hybrid predictor for ground-motion intensity with machine learning and conventional ground motion prediction equation
10.1038/s41598-020-68630-x · 2020 · External reference
Neural network-based equations for predicting PGA and PGV in Texas, Oklahoma, and Kansas
2018 · External reference
Ground motion prediction equations for distant subduction interface earthquakes based on empirical data in the Malay Peninsula and Japan
10.1016/j.soildyn.2018.03.024 · 2018 · External reference
Prediction of PGA in earthquake early warning using a long short-term memory neural network
10.1093/gji/ggad067 · 2023 · External reference
Ground motion amplification models for Japan using machine learning techniques
10.1016/j.soildyn.2020.106095 · 2020 · External reference
Ground-motion prediction by ANN using machine learning for the Tohoku region, Japan
2018 · External reference
Hybrid deep-learning network for rapid on-site peak ground velocity prediction
10.1109/tgrs.2022.3230829 · 2022 · External reference
The 2015 Gorkha Nepal earthquake: insights from earthquake damage survey
10.3389/fbuil.2015.00008 · 2015 · External reference
Earthquake forecast as a machine learning problem for imbalanced datasets: example of Georgia, Caucasus
10.3389/feart.2022.847808 · 2022 · External reference
A coarse-grained parallel approach for seismic damage simulations of urban areas based on refined models and GPU/CPU cooperative computing
10.1016/j.advengsoft.2014.01.010 · 2014 · External reference
Machine Learning Based Structural Seismic Damage Detection From Monitoring Acceleration Data
2020 · External reference
Unresolved reference
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A new attenuation relation for strong ground motion in Japan based on recorded data
10.1785/0120050138 · 2006 · External reference
Tensile strength equation for HSS bracing members having slotted end connections
10.1002/eqe.665 · 2007 · External reference
Incremental dynamic analysis
10.1002/eqe.141 · 2002 · External reference
The use of real earthquake accelerograms as input to dynamic analysis
10.1080/13632460409350521 · 2004 · External reference
Selection of ground motion time series and limits on scaling
10.1016/j.soildyn.2005.07.001 · 2006 · External reference
Does amplitude scaling of ground motion records result in biased nonlinear structural drift responses?
10.1002/eqe.695 · 2007 · External reference
Building specific damage estimation
1992 · External reference
Multi-hazard loss estimation methodology: earthquake model
2003 · External reference
Unresolved reference
2001 · External reference
ST4_Method_Random_Forest
10.1023/a:1010933404324 · 2001 · External reference
Random forests: from early developments to recent advancements
10.1080/21642583.2014.956265 · 2014 · External reference
Bagging predictors
10.1007/bf00058655 · 1996 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
Unresolved reference
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Unresolved reference
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Real-time physics-based regional earthquake disaster simulation using structural monitoring data
2020 · External reference
Unresolved reference
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Revised attenuation relation for peak horizontal acceleration using a new data base
1992 · External reference
Further analysis of the influence of site conditions and earthquake magnitude on ground-motion within-earthquake correlation: analysis of PGA and PGV data from the K-NET and the KiK-net (Japan) networks
10.1007/s10518-013-9493-9 · 2013 · External reference
Liquefaction hazard zonation mapping of the Saitama City, Japan
10.3126/jngs.v40i0.23598 · 2010 · External reference
