Hyo Seon Park

Professor

Yonsei University

South Korea

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About

Professor Hyo Seon Park is a faculty member at Yonsei University, South Korea. His research interests include metamaterials, vibration control in structures, and artificial intelligence applications in structural analysis. Recent publications focus on low-frequency resonance properties of cantilever-type metamaterials, as well as innovative methods for modal identification and damage localization in building structures.

Articles (17)

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2025

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2025

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2025

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2024

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2024

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2024

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2024

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2022

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2021

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2020

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2020

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2020

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2020

Collaborators (1)

Byung Kwan Oh

Assistant Professor

Jeonju University

SOUTH KOREA

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Hyo Seon Park

Research Interests

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About

Articles (17)

Low-frequency local resonance bandgap formation and flexural wave control of cantilever-type metamaterial-coupled structures

Low-frequency flexural vibration behavior of Euler–Bernoulli beams coupled with membrane-type metamaterials

Portable measurement system for modal identification using synchronized smartphones

Estimation of concrete compressive strength from non-destructive tests using a customized neural network and genetic algorithm

Hybrid behaviors of RC metaslab combining bandgap and isolation for broadband vibration control

Low-frequency vibration and noise control in sandwiched composite locally resonant metamaterials-embedded plate structures

Mapping top-two-floor corner coordinates to building strains in deep latent space

LSTM‐Based Approach for Stable Identification of Modal Damping Ratio in Building Structures

Artificial intelligence-based damage localization method for building structures using correlation of measured structural responses

Development and practical application of locally resonant metamaterials for attenuation of noise and flexural vibration of floors in residential buildings

Development of a real-time automated monitoring system for managing the hazardous environmental pollutants at the construction site

Towards environmental sustainability in the local community: Future insights for managing the hazardous pollutants at construction sites

Eco-friendly and economically optimal design model (EEODM) to reduce the CO2 emissions and the cost of long-span waffle slabs

Convolutional neural network–based data recovery method for structural health monitoring

Seismic response prediction method for building structures using convolutional neural network

Damage localization method for building structures based on the interrelation of dynamic displacement measurements using convolutional neural network

Convolutional neural network-based safety evaluation method for structures with dynamic responses

Collaborators (1)

Byung Kwan Oh

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