Sunjoong Kim

Assistant Professor

Seoul National University

South Korea

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About

Sunjoong Kim is an Assistant Professor at the University of Seoul, South Korea. His research focuses on vibration-based damage detection and assessment, with recent articles exploring advanced methodologies such as deep learning, data synthesis, and hybrid models for structural health monitoring. His work includes investigating low-frequency cable vibrations and enhancing detection techniques using various neural network approaches.

Articles (18)

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2025

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2025

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2024

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2021

Collaborators (2)

Ho-Kyung Kim

Professor

Seoul National University

SOUTH KOREA

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Hyungchul Yoon

Associate Professor

Chungbuk National University

SOUTH KOREA

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Sunjoong Kim

Research Interests

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Articles (18)

Hybrid CAE-DSVDD for unsupervised vibration-based damage detection in in situ steel truss bridge

Enhancing field applicability of unsupervised vibration-based damage assessment via time–frequency pattern differentiation

Pointwise vortex-induced vibration detection: Learning from synthetic time-series data

Field observation and cause investigation of low-frequency cable vibrations in a cable-stayed bridge

Enhancing Vibration-based Damage Assessment with 1D-CNN: Parametric Studies and Field Applications

Vibration detection of stay-cable from low-quality CCTV images using deep-learning-based dehazing and semantic segmentation algorithms

Deep-learning-based data loss reconstruction for spatiotemporal temperature in piloti structures: Enhancing applicability with limited datasets

Deep Gaussian process regression for damping of a long-span bridge under varying environmental and operational conditions

Unsupervised Vortex-induced Vibration Detection Using Data Synthesis

Pointwise multiclass vibration classification for cable-supported bridges using a signal-segmentation deep network

Vision‐based automatic cable displacement measurement using Cable‐ROI Net and Uni‐KLT

Vision-Based Cable Displacement Measurement Using Side View Video

Design and implementation of a SHM system for a heritage timber building

High-mode vortex-induced vibration of stay cables: monitoring, cause investigation, and mitigation

Automated damping identification of long-span bridge using long-term wireless monitoring data with multiple sensor faults

Using supervised learning techniques to automatically classify vortex-induced vibration in long-span bridges

Machine-learning-based prediction of vortex-induced vibration in long-span bridges using limited information

Effect of Environmental and Operational Conditions on the Modal Parameters of Twin Cable-stayed Bridge

Collaborators (2)

Ho-Kyung Kim

Hyungchul Yoon

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