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Jong-Su Jeon

Jong-Su Jeon

D-Index & Metrics

Engineering and Technology

D-Index
37
Citations
6264
World Ranking
8334
National Ranking
221

Overview

Jong-Su Jeon is affiliated with Hanyang University in South Korea, specializing in engineering with a focus on civil and structural engineering. Their research encompasses several subfields, particularly in building and construction, materials chemistry, artificial intelligence, and ocean engineering.

The scope of Jeon's work primarily covers topics related to seismic performance and analysis, structural behavior of reinforced concrete, structural health monitoring techniques, infrastructure maintenance and monitoring, and structural response to dynamic loads. Their research also addresses concrete corrosion and durability as well as masonry and concrete structural analysis.

Jeon's publications have appeared frequently in several academic venues. These include:

  • Engineering Structures
  • Earthquake Engineering & Structural Dynamics
  • Journal of Building Engineering
  • Journal of Structural Engineering
  • Construction and Building Materials

Notable recent papers authored by Jeon include:

  • Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach, 2020, Engineering Structures
  • Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls, 2020, Engineering Structures
  • Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames, 2020, Journal of Building Engineering
  • Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement, 2021, Journal of Building Engineering
  • Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems, 2021, Engineering Structures

Frequent collaborators in Jeon's research include:

  • Sujith Mangalathu
  • Chang Seok Lee
  • Eunsoo Choi
  • Seong-Hoon Hwang
  • Bilal Ahmed

The integration of machine learning methods into the assessment and prediction of structural and seismic behavior is a persistent theme in Jeon's publications. Their studies often combine data-driven techniques with structural engineering principles to evaluate the performance and failure modes of reinforced concrete elements and infrastructure systems under dynamic loading conditions.

Best Publications

  • Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach

    Sujith Mangalathu;Seong Hoon Hwang;Jong Su Jeon

  • Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques

    Sujith Mangalathu;Jong-Su Jeon

  • Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls

    Sujith Mangalathu;Hansol Jang;Seong Hoon Hwang;Jong Su Jeon

  • Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes

    Sujith Mangalathu;Gwanghee Heo;Jong Su Jeon

  • Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study

    Sujith Mangalathu;Jong-Su Jeon

  • Rapid seismic damage evaluation of bridge portfolios using machine learning techniques

    Sujith Mangalathu;Seong-Hoon Hwang;Eunsoo Choi;Jong-Su Jeon

  • Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression

    Sujith Mangalathu;Jong Su Jeon;Reginald DesRoches

  • Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames

    Seong Hoon Hwang;Sujith Mangalathu;Jiuk Shin;Jong Su Jeon

  • Data‐driven rapid damage evaluation for life‐cycle seismic assessment of regional reinforced concrete bridges

    Unknown

  • Framework of aftershock fragility assessment–case studies: older California reinforced concrete building frames

    Jong-Su Jeon;Reginald DesRoches;Laura N. Lowes;Ioannis Brilakis

  • Fragility curves for non-ductile reinforced concrete frames that exhibit different component response mechanisms

    Jong-Su Jeon;Laura N. Lowes;Reginald DesRoches;Ioannis Brilakis

  • Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement

    Sujith Mangalathu;Hanbyeol Shin;Eunsoo Choi;Jong Su Jeon

  • Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques

    Jong-Su Jeon;Abdollah Shafieezadeh;Reginald DesRoches

  • Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems

    Sujith Mangalathu;Karthika Karthikeyan;De-Cheng Feng;Jong-Su Jeon

  • Stripe-based fragility analysis of multispan concrete bridge classes using machine learning techniques

    Sujith Mangalathu;Jong Su Jeon

  • Optimal Intensity Measures in Probabilistic Seismic Demand Models of Cable-Stayed Bridges Subjected to Pulse-Like Ground Motions

    Jian Zhong;Jong-Su Jeon;Ya-Hui Shao;Liang Chen

  • Parameterized Seismic Fragility Curves for Curved Multi-frame Concrete Box-Girder Bridges Using Bayesian Parameter Estimation

    Jong-Su Jeon;Sujith Mangalathu;Junho Song;Reginald Desroches

  • Seismic fragility of lightly reinforced concrete frames with masonry infills

    Jong-Su Jeon;Ji Hun Park;Reginald Desroches

  • Impact of Spatial Variability Parameters on Seismic Fragilities of a Cable-Stayed Bridge Subjected to Differential Support Motions

    Jian Zhong;Jong-Su Jeon;Wancheng Yuan;Reginald DesRoches

  • An innovative seismic bracing system based on a superelastic shape memory alloy ring

    Nan Gao;Jong-Su Jeon;Darel E Hodgson;Reginald DesRoches

  • Automated Damage Index Estimation of Reinforced Concrete Columns for Post-Earthquake Evaluations

    Stephanie G. Paal;Jong-Su Jeon;Ioannis Brilakis;Reginald DesRoches

  • ANCOVA-based grouping of bridge classes for seismic fragility assessment

    Sujith Mangalathu;Jong-Su Jeon;Jamie E. Padgett;Reginald DesRoches

Frequent Co-Authors

Sujith Mangalathu
Sujith Mangalathu Georgia Institute of Technology
Reginald DesRoches
Reginald DesRoches Rice University
Laura N. Lowes
Laura N. Lowes University of Washington
Ioannis Brilakis
Ioannis Brilakis University of Cambridge
Jamie E. Padgett
Jamie E. Padgett Rice University
Woo Jin Kim
Woo Jin Kim Hongik University
David Scott
David Scott Ascension Health
Wei-Xin Ren
Wei-Xin Ren Shenzhen University
Junho Song
Junho Song Seoul National University
De-Cheng Feng
De-Cheng Feng Southeast University

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