Jong-Su Jeon
Jong-Su Jeon
Associate Professor of Civil and Environmental Engineering, Hanyang University
Verified email at - Homepage
Cited by
Cited by
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
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
Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach
S Mangalathu, SH Hwang, JS Jeon
Engineering Structures 219, 110927, 2020
Fragility curves for non-ductile reinforced concrete frames that exhibit different component response mechanisms
JS Jeon, LN Lowes, R DesRoches, I Brilakis
Engineering Structures 85, 127–143, 2015
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
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
Framework of aftershock fragility assessment–case studies: older California reinforced concrete building frames
JS Jeon, R DesRoches, I Lowes, Laura N., Brilakis
Earthquake Engineering and Structural Dynamics 44 (15), 2617–2636, 2015
Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls
S Mangalathu, H Jang, SH Hwang, JS Jeon
Engineering Structures 208, 110331, 2020
Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques
JS Jeon, A Shafieezadeh, R DesRoches
Earthquake Engineering & Structural Dynamics 43 (14), 2075–2095, 2014
Rapid seismic damage evaluation of bridge portfolios using machine learning techniques
S Mangalathu, SH Hwang, E Choi, JS Jeon
Engineering Structures 201, 109785, 2019
An innovative seismic bracing system based on a superelastic shape memory alloy ring
N Gao, JS Jeon, D Hodgson, R DesRoches
Smart Materials and Structures 25 (5), 055030, 2016
Seismic fragility of lightly reinforced concrete frames with masonry infills
JS Jeon, JH Park, DR Reginald
Earthquake Engineering and Structural Dynamics 44 (11), 1783–1803, 2015
Damage assessment of older highway bridges subjected to three-dimensional ground motions: Characterization of shear-axial force interaction on seismic fragilities
JS Jeon, A Shafieezadeh, DH Lee, E Choi, R DesRoches
Engineering Structures 87, 45-57, 2015
Stripe‐based fragility analysis of multispan concrete bridge classes using machine learning techniques
S Mangalathu, JS Jeon
Earthquake Engineering and Structural Dynamics 48 (11), 1238–1255, 2019
ANCOVA-based grouping of bridge classes for seismic fragility assessment
S Mangalathu, JS Jeon, JE Padgett, R DesRoches
Engineering Structures 123, 379–394, 2016
Geometric parameters affecting seismic fragilities of curved multi-frame concrete box-girder bridges with integral abutments
JS Jeon, R DesRoches, T Kim, E Choi
Engineering Structures 122, 121-143, 2016
Machine vision-enhanced postearthquake inspection
S German, JS Jeon, Z Zhu, C Bearman, I Brilakis, R DesRoches, L Lowes
Journal of Computing in Civil Engineering 27 (6), 622-634, 2013
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
Automated damage index estimation of reinforced concrete columns for post-earthquake evaluations
SG Paal, JS Jeon, I Brilakis, R DesRoches
Journal of Structural Engineering 141 (9), 04014228, 2015
Optimal intensity measures in probabilistic seismic demand models of cable-stayed bridges subjected to pulse-like ground motions
J Zhong, JS Jeon, YH Shao, L Chen
Journal of Bridge Engineering 24 (2), 04018118, 2019
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