Pang‐jo Chun 교수

The evaluation of internal damage in concrete structures is related to not only its rapid repair and reinforcement but also its safe usage, and is therefore essential for ensuring its longevity. This paper proposes a method to evaluate the extent of internal damage due to rebar corrosion using Random Forest, one of the supervised machine learning methods. In supervised machine learning, appropriate inputs should be identified to obtain accurate results. This research uses air permeability coeffi

Spalling of concrete fragments due to the deterioration of concrete structures can cause property damage or serious and even fatal accidents; thus, there is a need to detect such deterioration. Generally, the hammering test is employed as the main inspection method to prevent such concrete spalling; however, it requires close contact with the structure being tested. Getting close to the structure for inspection is expensive and time consuming, and if the structure is high up, there is a risk of

Focusing on learning-based semantic segmentation (SS) methods for bridge point cloud data (PCD), this study proposes a structure-oriented concept (SOC) with training focused on the spatial distribution patterns of bridge components, including both the horizontally absolute location of each component and its vertically relative position compared with other components. Then a structure-oriented loss (SOL) function, which embodies the core of SOC, is defined accordingly, and it is compared to five

We proposed an automatic detection method of slope failure regions using a semantic segmentation method called Mask R-CNN based on a deep learning algorithm to improve the efficiency of damage assessment in the event of slope failure disaster. There is limited research on detecting landslides by deep learning, and the lack of training data is an important issue to be resolved, as aerial photographs are not taken with sufficient frequency during a disaster. This study attempts to use CutMix-based

Slurry infiltrated fiber concrete (SIFCON) have received considerable attention in recent years. The SIFCON is distinguished from the conventional steel fiber reinforced cementitious composite (FRCC) by its high volume ratio of fibers, far beyond that of typical steel FRCC. Although this material has already been used for important structures including power plants and military facilities, very little is known about its behavior under blast loading. We therefore have experimentally investigated

Climate change exacerbates natural disasters, demanding rapid damage and risk assessment. However, expert-reliant analyses delay responses despite drone-aided data collection. This study develops and compares multimodal AI approaches using advanced large language models (LLMs) for expert-level landslide image analysis. We tackle landslide-specific challenges: capturing nuanced geotechnical reasoning beyond data digitization (specific to geological features and risk assessment), developing specia

Accurate estimation of dimensions for bridge components is essential for maintaining infrastructure and ensuring safety. This study proposes an automated framework that integrates deep learning-based semantic segmentation with geometric analysis for bridge component dimension estimation from point cloud data. The framework comprises two main stages: (1) a semantic segmentation module based on the PointNet++ architecture with MSG, enhanced with the improved structure-oriented concept (SOC) for im

This paper reports on a forensic investigation following the collapse of the Iwama Ohashi Bridge over the Shimanto River in Japan. The central section of the bridge failed in 2017 when a steel pier buckled. The investigation included thickness measurements using a new ultrasensitive magnetic non-destructive device. This revealed that the buckled pier and other bridge piers had been significantly abraded by fast-moving gravel. As a result, the steel plate of the downstream faces near the riverbed

Complex image backgrounds often compromise the reliability of damage detection. In bridge inspection, a further challenge lies in accurately recording and localizing the detected damage onto a 3D model. Based on image and point cloud data (PCD) fusion, this paper proposes a five-step methodology for detecting bridge damage and registering it on a 3D model. High-quality images and PCD files are simultaneously collected using a LiDAR 3D camera with their relationships clearly recorded. The complet

Accurate detection and localization of steel corrosion in tunnel infrastructure remains a major challenge, particularly under conditions of variable lighting, limited accessibility, and visual domain shifts common in real-world inspection scenarios. This study presents a novel integrated framework that automates tunnel inspection by combining self-supervised deep learning, image-based three-dimensional reconstruction, and building information modeling (BIM)-based spatial damage localization. At

This paper presents analytical solutions for the problem of skewed thick plates under transverse load on a Winkler foundation, which has not been reported in the literature. The thick plate solution is obtained by using a framework of an oblique coordinate system. First, the governing differential equation in that system is derived, and the solution is obtained using deflection and rotation as derivatives of the potential function developed here. This method is applicable for arbitrary loading c

The Thirteenth East Asia-Pacific Conference on Structural Engineering and Construction (EASEC-13), September 11-13, 2013, Sapporo, Japan.

This paper presents analytical solutions for skewed thick plates under transverse loading that have previously been unreported in the literature. The thick plate solution is obtained in a framework of an oblique coordinate system. The governing equation is first derived in the oblique coordinate system, and the solution is obtained using deflection and rotation as partial derivatives of a potential function developed in this research. The solution technique is applied to three illustrative appli