Sujith Mangalathu Ph.D
Sujith Mangalathu Ph.D
Researcher
Verified email at gatech.edu - Homepage
Title
Cited by
Cited by
Year
Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes
S Mangalathu, G Heo, JS Jeon
Engineering Structures 162, 166-176, 2018
562018
Predicting the dissolution kinetics of silicate glasses using machine learning
NM Krishnan, S Mangalathu, MM Smedskjaer, A Tandia, H Burton, ...
Journal of Non-Crystalline Solids 487, 37-45, 2018
562018
Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques
S Mangalathu, JS Jeon
Engineering Structures 160, 85-94, 2018
562018
Review of strength models for masonry spandrels
K Beyer, S Mangalathu
Bulletin of Earthquake Engineering 11 (2), 521-542, 2013
562013
Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression
S Mangalathu, JS Jeon, R DesRoches
Earthquake Engineering and Structural Dynamics 47 (3), 784-801, 2018
502018
PERFORMANCE BASED GROUPING AND FRAGILITY ANALYSIS OF BOX-GIRDER BRIDGES IN CALIFORNIA
S Mangalathu
Georgia Institute of Technology, 2017
502017
ANCOVA-based grouping of bridge classes for seismic fragility assessment
S Mangalathu, JS Jeon, JE Padgett, R DesRoches
Engineering Structures 123, 379-394, 2016
392016
Permeable Piles: An Alternative to Improve the Performance of Driven Piles
P Ni, S Mangalathu, G Mei, Y Zhao
Computers and Geotechnics 84, 78-87, 2017
342017
Machine learning–based failure mode recognition of circular reinforced concrete bridge columns: Comparative study
S Mangalathu, JS Jeon
Journal of Structural Engineering 145 (10), 04019104, 2019
312019
Parameterized seismic fragility curves for curved multi-frame concrete box-girder bridges using Bayesian parameter estimation
JS Jeon, S Mangalathu, J Song, R Desroches
Journal of Earthquake Engineering 23 (6), 954-979, 2019
302019
Stripe‐based fragility analysis of multispan concrete bridge classes using machine learning techniques
S Mangalathu, JS Jeon
Earthquake Engineering & Structural Dynamics 48 (11), 1238-1255, 2019
252019
Fragility analysis of gray iron pipelines subjected to tunneling induced ground settlement
P Ni, S Mangalathu
Tunnelling and Underground Space Technology 76, 133-144, 2018
232018
Bridge classes for regional seismic risk assessment: Improving HAZUS models
S Mangalathu, F Soleimani, JS Jeon
Engineering Structures 148, 755-766, 2017
232017
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
212020
Rapid seismic damage evaluation of bridge portfolios using machine learning techniques
S Mangalathu, SH Hwang, E Choi, JS Jeon
Engineering Structures 201, 109785, 2019
212019
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
202019
Numerical study on the peak strength of masonry spandrels with arches
K Beyer, S Mangalathu
Journal of Earthquake Engineering 18 (2), 169-186, 2014
202014
Displacement-dependent lateral earth pressure models
P Ni, S Mangalathu, L Song, G Mei, Y Zhao
Journal of Engineering Mechanics 144 (6), 04018032, 2018
192018
Laboratory investigation of pore pressure dissipation in clay around permeable piles
P Ni, S Mangalathu, G Mei, Y Zhao
Canadian Geotechnical Journal 55 (9), 1257-1267, 2018
192018
Performance‐based grouping methods of bridge classes for regional seismic risk assessment: Application of ANOVA, ANCOVA, and non‐parametric approaches
S Mangalathu, JS Jeon, JE Padgett, R DesRoches
Earthquake Engineering & Structural Dynamics 46 (14), 2587-2602, 2017
182017
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