Sujith Mangalathu
Sujith Mangalathu
Researcher
Verified email at ucla.edu - Homepage
Title
Cited by
Cited by
Year
Review of strength models for masonry spandrels
K Beyer, S Mangalathu
Bulletin of Earthquake Engineering 11 (2), 521-542, 2013
502013
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
442018
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
432018
PERFORMANCE BASED GROUPING AND FRAGILITY ANALYSIS OF BOX-GIRDER BRIDGES IN CALIFORNIA
S Mangalathu
Georgia Institute of Technology, 2017
412017
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
402018
ANCOVA-based grouping of bridge classes for seismic fragility assessment
S Mangalathu, JS Jeon, JE Padgett, R DesRoches
Engineering Structures 123, 379-394, 2016
352016
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
332018
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
332017
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
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
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
182018
Numerical study on the peak strength of masonry spandrels with arches
K Beyer, S Mangalathu
Journal of Earthquake Engineering 18 (2), 169-186, 2014
182014
Bridge classes for regional seismic risk assessment: Improving HAZUS models
S Mangalathu, F Soleimani, JS Jeon
Engineering Structures 148, 755-766, 2017
172017
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
132019
Displacement-dependent lateral earth pressure models
P Ni, S Mangalathu, L Song, G Mei, Y Zhao
Journal of Engineering Mechanics 144 (6), 04018032, 2018
132018
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
132017
A comparative analytical study on the fragility assessment of box-girder bridges with various column shapes
F Soleimani, S Mangalathu, R DesRoches
Engineering Structures 153, 460-478, 2017
122017
Reliability analysis of counterfort retaining walls
AK Mandali, MS Sujith, BN Rao, J Maganti
Electronic Journal of Structural Engineering 11 (1), 42-56, 2011
122011
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
112020
Rapid seismic damage evaluation of bridge portfolios using machine learning techniques
S Mangalathu, SH Hwang, E Choi, JS Jeon
Engineering Structures 201, 109785, 2019
112019
The system can't perform the operation now. Try again later.
Articles 1–20