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Henry V Burton Ph.D., S.E.
Henry V Burton Ph.D., S.E.
Verified email at seas.ucla.edu - Homepage
Title
Cited by
Cited by
Year
Machine learning applications for building structural design and performance assessment: State-of-the-art review
H Sun, HV Burton, H Huang
Journal of Building Engineering 33, 101816, 2021
3232021
Statistical procedures for developing earthquake damage fragility curves
D Lallemant, A Kiremidjian, H Burton
Earthquake Engineering & Structural Dynamics 44 (9), 1373-1389, 2015
2762015
Framework for incorporating probabilistic building performance in the assessment of community seismic resilience
HV Burton, G Deierlein, D Lallemant, T Lin
Journal of Structural Engineering 142 (8), C4015007, 2016
2222016
A machine learning framework for assessing post-earthquake structural safety
Y Zhang, HV Burton, H Sun, M Shokrabadi
Structural safety 72, 1-16, 2018
1942018
Classifying earthquake damage to buildings using machine learning
S Mangalathu, H Sun, CC Nweke, Z Yi, HV Burton
Earthquake Spectra 36 (1), 183-208, 2020
1812020
Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning
H Huang, HV Burton
Journal of Building Engineering 25, 100767, 2019
1232019
Simulation of seismic collapse in nonductile reinforced concrete frame buildings with masonry infills
H Burton, G Deierlein
Journal of Structural Engineering 140 (8), A4014016, 2014
1192014
Predicting the dissolution kinetics of silicate glasses using machine learning
NMA Krishnan, S Mangalathu, MM Smedskjaer, A Tandia, H Burton, ...
Journal of Non-Crystalline Solids 487, 37-45, 2018
1182018
Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions
S Mangalathu, HV Burton
International Journal of Disaster Risk Reduction 36, 101111, 2019
892019
Risk-based assessment of aftershock and mainshock-aftershock seismic performance of reinforced concrete frames
M Shokrabadi, HV Burton
Structural Safety 73, 64-74, 2018
722018
Estimating aftershock collapse vulnerability using mainshock intensity, structural response and physical damage indicators
HV Burton, S Sreekumar, M Sharma, H Sun
Structural safety 68, 85-96, 2017
632017
Community of practice for modeling disaster recovery
SB Miles, HV Burton, H Kang
Natural Hazards Review 20 (1), 04018023, 2019
602019
Seismic drift demand estimation for steel moment frame buildings: From mechanics-based to data-driven models
X Guan, H Burton, M Shokrabadi, Z Yi
journal of structural engineering 147 (6), 04021058, 2021
532021
Development and utilization of a database of infilled frame experiments for numerical modeling
H Huang, HV Burton, S Sattar
Journal of Structural Engineering 146 (6), 04020079, 2020
512020
Pattern recognition approach to assess the residual structural capacity of damaged tall buildings
Y Zhang, HV Burton
Structural safety 78, 12-22, 2019
472019
Seismic performance of a self-centering steel moment frame building: From component-level modeling to economic loss assessment
X Guan, H Burton, S Moradi
Journal of Constructional Steel Research 150, 129-140, 2018
472018
Impact of sequential ground motion pairing on mainshock-aftershock structural response and collapse performance assessment
M Shokrabadi, HV Burton, JP Stewart
Journal of Structural Engineering 144 (10), 04018177, 2018
462018
Integrating performance-based engineering and urban simulation to model post-earthquake housing recovery
HV Burton, SB Miles, H Kang
Earthquake Spectra 34 (4), 1763-1785, 2018
442018
Measuring the impact of enhanced building performance on the seismic resilience of a residential community
HV Burton, G Deierlein, D Lallemant, Y Singh
Earthquake spectra 33 (4), 1347-1367, 2017
432017
Reconstructing seismic response demands across multiple tall buildings using kernel‐based machine learning methods
H Sun, H Burton, J Wallace
Structural Control and Health Monitoring 26 (7), e2359, 2019
382019
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