SeungHee Park
Sungkyunkwan University · 工学
研究室紹介
Professor SeungHee Park's research lab specializes in structural health monitoring (SHM) and non-destructive evaluation (NDE) technologies, with a focus on developing advanced sensing and signal processing techniques for civil and mechanical infrastructure. The lab pioneers the use of piezoelectric materials—particularly PZT and MFC patches—combined with data-driven methods such as principal component analysis (PCA), k-means clustering, and deep learning for real-time damage detection in steel structures and wire ropes. A key research direction involves improving early warning systems for natural disasters, especially wildfires, through AI-powered image analysis and synthetic data generation to address data imbalance in training. The lab also explores innovative sensing modalities, including impedance-based methods and magnetic flux leakage (MFL) techniques, for reliable in-situ condition assessment of critical infrastructure.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15This article presents a practical method for an electro-mechanical impedance-based wireless structural health monitoring (SHM), which incorporates the principal component analysis (PCA)-based data compression and k-means clustering-based pattern recognition. An on-board active sensor system, which consists of a miniaturized impedance measuring chip (AD5933) and a self-sensing macro-fiber composite (MFC) patch, is utilized as a next-generation toolkit of the electromechanical impedance-based SHM
This paper presents the results of experimental studies on piezoelectric lead-zirconate–titanate (PZT)-based active damage detection techniques for nondestructive evaluations (NDE) of steel bridge components. PZT patches offer special features suitable for real-time in situ health monitoring systems for large and complex steel structures, because they are small, light, cheap, and useful as built-in sensor systems. Both impedance and Lamb wave methods are considered for damage detection of lab-si
In order to protect human lives and infrastructure, as well as to minimize the risk of damage, it is important to predict and respond to natural disasters in advance. However, currently, the standardized disaster response system in South Korea still needs further advancement, and the response phase systems need to be improved to ensure that they are properly equipped to cope with natural disasters. Existing studies on intelligent disaster management systems (IDSSs) in South Korea have focused on
This paper presents the results of a feasibility study on an impedance-based damage detection technique using thickness modes of piezoelectric (PZT) patches for steel structures. It is newly proposed to analyze the changes of the impedances of the thickness modes (frequency range > 1 MHz) at the PZT based on its resonant frequency shifts rather than those of the lateral modes (frequency range > 20 kHz) at the PZT based on its root mean square (RMS) deviations, since the former gives more signifi
In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial da
To minimize the damage caused by wildfires, a deep learning-based wildfire-detection technology that extracts features and patterns from surveillance camera images was developed. However, many studies related to wildfire-image classification based on deep learning have highlighted the problem of data imbalance between wildfire-image data and forest-image data. This data imbalance causes model performance degradation. In this study, wildfire images were generated using a cycle-consistent generati
The active sensing methods using piezoelectric materials have been extensively investigated for the efficient use in structural health monitoring (SHM) applications. Relying on high frequency structural excitations, the methods showed the extreme sensitivity to minor defects in a structure. Recently, a sensor self-diagnostic procedure that performs in situ monitoring of the operational status of piezoelectric (PZT) active sensors and actuators in SHM applications has been proposed. In this inves
Although there has been study on worker detection using computer vision (CV) for the safety of construction sites, it is still challenging to identify employees who are obstructed or have poor vision. To solve these problems, we propose a method of small and overlapping target (worker) detection at a complex construction site named SOC-YOLO. The method is based on YOLOv5 and utilizes distance intersection over union (DIoU) non-maximum suppression (NMS), incorporating weighted triplet attention,
Estimating the damaged area after a forest fire is important for responding to this natural catastrophe. With the support of aerial remote sensing, typically with unmanned aerial vehicles (UAVs), the aerial imagery of forest-fire areas can be easily obtained; however, retrieving the burnt area from the image is still a challenge. We implemented a new approach for segmenting burnt areas from UAV images using deep learning algorithms. First, the data were collected from a forest fire in Andong, th
Economic and reliable online health monitoring strategies are very essential for safe operation of civil, mechanical and aerospace structures. This study presents online structural health monitoring (SHM) techniques using wireless impedance sensor nodes equipped with both functions of structural damage identification and sensor self-diagnosis. The wireless impedance sensor node incorporating a miniaturized impedance measuring chip, a microcontroller and radio-frequency (RF) telemetry is equipped
Nondestructive evaluation (NDE) of steel cables in long span bridges is necessary to prevent structural failure. Thus, an automated cable monitoring system is proposed that uses a suitable NDE technique and a cable-climbing robot. A magnetic flux leakage- (MFL-) based inspection system was applied to monitor the condition of cables. This inspection system measures magnetic flux to detect the local faults (LF) of steel cable. To verify the feasibility of the proposed damage detection technique, a