Changhwan Jang

Spectral wave models such as SWAN provide reliable harbor wave fields but require repeated, computationally expensive runs during preliminary design. This study develops a domain-knowledge-guided, U-Net-based surrogate model that rapidly approximates two-dimensional wave fields from SWAN simulations. The model takes water depth, incident significant wave height, incident wave direction, and a signed distance field (SDF) encoding structure geometry as inputs, and it combines a dynamic-resolution preprocessing scheme that preserves the terrain aspect ratio with a shadow-weighted loss function that emphasizes the sheltered zone behind breakwaters. Trained on 240 SWAN scenarios that combine 8 idealized geometries with 5 incident directions and 6 wave heights (about 15.7 million grid points), the model reproduced the SWAN significant wave height with a coefficient of determination of R² = 0.8807 and a mean absolute error of 0.07 m over the whole domain under an untrained but within-range (interpolated) wave height condition, with comparable accuracy in the region of interest behind breakwaters (ROI R² = 0.85). In a real sea application to Sokcho Harbor benchmarked against SWAN rather than field data, the model reproduced the offshore to harbor wave height pattern; along entrance and propagation-axis transects, it followed the SWAN profiles with high correlation (R ≈ 0.94–0.95), while overestimating the innermost calm zone waves by a non-negligible margin. The model is thus suited to rapid wave-field screening in preliminary harbor design.

Chloride penetration depth (CPD) is a critical durability indicator for concrete structures, yet experimental data are often limited. This study evaluates whether increasing model complexity is beneficial under such constraints by comparing six machine learning and deep learning models—extreme gradient boosting, categorical boosting (CATB), random forest, multilayer perceptron (MLP), deep neural network (DNN), and a hybrid model combined with CATB and DNN (CatDNN)—using a dataset of 1078 cases. During training, CatDNN exhibited the earliest stabilization, reaching the best epoch at 40, while MLP and DNN stabilized after approximately 30 epochs. However, overfitting tracking revealed a flat tendency near 40 epochs for CatDNN, indicating potential data saturation. The test results showed small performance differences among all the models. CatDNN achieved the lowest max error (1.21), demonstrating effective residual correction, but its R² (0.9123) was slightly lower than that of DNN (0.9129), suggesting that increased complexity did not yield meaningful improvement. The validation results confirmed high reliability across all the models (R² ≥ 0.88). Overall, the findings indicate that, under limited data conditions, simple and well-fitted models can outperform or match complex hybrid architectures, emphasizing the importance of model efficiency over structural complexity.

Groundwater discharge is critical for maintaining river flow during dry seasons, especially in lowland areas. Despite its significance, groundwater resources have often been overlooked highlighting the need for comprehensive studies amidst growing pressure to develop new water resources. This study focuses on the Soyang River Basin, South Korea, including its ungauged northern regions, the nearby DMZ (Demilitarized Zone), using the physically based Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model. A three-year simulation was conducted to examine variable aquifer depth distribution patterns by assuming an inverse relationship between surface elevation and aquifer bottom depth. Three case studies (i.e., equal distribution, linear regression, and logarithmic regression) were evaluated and compared. The method to identity optimal aquifer depth distributions to enhance groundwater simulation accuracy in regions with significant topographical variation was incorporated. Groundwater levels at six monitoring sites showed that altitude-based variable aquifer depths outperformed the equal distribution case. The results showed strong agreement between simulated and observed values, particularly in the linear regression case with an R-squared statistic of 0.858 and Nash–Sutcliffe Efficiency index of 0.789, indicating that linear regression-based aquifer depth estimation can significantly improves long-term runoff modeling and groundwater simulation accuracy. The logarithmic regression case had the lowest relative peak error in peak flow. These findings highlight the importance of adjusting aquifer depth distributions in physically based hydrologic models to better reflect real-world conditions. Overall, this study contributes to advance groundwater modeling by integrating variable aquifer depth distributions into a physically based hydrologic model for large scale watersheds.

In this study, a new articulated concrete armor unit named Couple-Lock, which can be enlarged in size to cope with high waves, is easy to secure binding force between units, and can be applied to various field conditions, was developed. The Couple-Lock consists of two symmetrical blocks. One symmetrical block has four legs installed in all directions on one end of the body. Since two left-right symmetrical blocks are connected with a wire rope and behave, a pair of blocks can be treated as a single block and enabling large size to cope with high waves. Also, the Couple-Lock responds flexibly to topographical changes. In order to examine the hydraulic performance and stability of the developed armor unit, a hydraulic model experiment was conducted. As a result, the average reflection coefficients of ordinary wave and storm wave conditions were calculated as 0.433 and 0.533, respectively. The average transmission coefficients under ordinary wave and storm wave conditions were calculated as 0.046 and 0.147, respectively. Among the 62 storm wave conditions, wave overtopping occurred in 50 storm waves. The stability factor of the Couple-Lock was calculated to be about 18, which is twice of the stability factor of the tetrapod.

In this study, the effect of tapioca starch (TP) on mortar was evaluated by incorporating TP into the mortar mixture. The evaluation involved analyzing the mortar’s quality characteristics, performance, and fundamental quality improvements. The addition of TP resulted in a decrease in flow, which was attributed to increased viscosity. Specifically, a 10% reduction in flow was observed with a 0.025% increase in TP content. After 28 days, the impact of TP on the compressive strength of the mortar remained consistent, regardless of the TP amount. However, within the first 3 days, higher TP content accelerated strength development, with early compressive strength increasing by up to 20% at a 0.050% TP level. Additionally, bond strength improved by approximately 60% at a 0.050% TP concentration, and final shrinkage was reduced by 5%.

Efficient energy use is crucial for achieving carbon neutrality and reduction. As part of these efforts, research is being carried out to apply a phase change material (PCM) to a concrete structure together with an aggregate. In this study, an energy consumption simulation was performed using data from concrete mock-up structures. To perform the simulation, the threshold investigation was performed through the Bayesian approach. Furthermore, the spiking part of the spiking neural network was modularized and integrated into a recurrent neural network (RNN) to find accurate energy consumption. From the training-test results of the trained neural network, it was possible to predict data with an R² value of 0.95 or higher through data prediction with high accuracy for the RNN. In addition, the spiked parts were obtained; it was found that PCM-containing concrete could consume 32% less energy than normal concrete. This result suggests that the use of PCM can be a key to reducing the energy consumption of concrete structures. Furthermore, the approach of this study is considered to be easily applicable in energy-related institutions and the like for predicting energy consumption during the summer.

The construction industry strives for sustainable solutions to tackle environmental challenges and optimize resource use. One such focus area is the management of industrial byproducts and waste materials, including fugitive dust control through wheel washers. While wheel washers play a pivotal role in dust management, they generate a challenging byproduct known as wheel washer sludge (WWS). The disposal of WWS is complicated due to its composition and potential hazards. Recent research explores reusing WWS in construction materials, particularly in repair mortar, aiming for sustainability and circular economy principles. This study investigates the incorporation of WWS into repair mortar formulations, evaluating mechanical properties, durability, and environmental implications. Results show that WWS enhances workability but prolongs setting time. Mechanical strength tests reveal improvements with WWS addition, especially when pretreated. Water absorption rates decrease with pretreated WWS, indicating enhanced durability. Chemical attack tests demonstrate resistance to carbonation and chloride penetration, especially with modified WWS. Freeze–thaw tests reveal improved resistance with WWS addition, particularly modified. Microstructure analysis confirms hydration products and denser matrices with WWS inclusion. Environmental hazard analysis shows WWS contains no harmful heavy metals, indicating its suitability for use in repairs. Overall, this study highlights the technical feasibility and environmental benefits of incorporating WWS into repair mortar, contributing to sustainable construction practices.

The aim of this study was to design operation methods for three-stage coil gun experiments and to fabricate and compare prototypes of control systems using these methods. Two methods are proposed: (1) recognizing the position of a projectile using a photointerrupter and (2) operating a control circuit to supply power to a silicon-controlled rectifier when the projectile reaches an intended position by registering the delay time between coil gun operations. The distance between the projectile and the solenoid coil during a coil gun operation is a key design factor. For the multi-stage coil gun manufactured in this study, the discharge time must be determined according to the position of the projectile, which moves at high velocity. Therefore, the selected method should have minimal operation error and allow the circuit to be easily used according to the coil gun stage and configuration. This study compares the prototypes of coil gun discharge circuits that were fabricated based on the proposed methods by applying them to three-stage coil guns and measuring their velocities. The findings of this study could be used to suggest design methods for experimental models for coil guns with fewer stages according to the final velocity and coil gun stage to be manufactured.

The edge wave on a uniform-sloped seabed was described by the velocity-potential function by Mok and Yeh in 1999. Edge waves cannot be extended above a certain level from the still-water level, and the upper limit of the run-up of the edge waves for given conditions is found here. In this study, quantitative mass transport by the edge waves of the beach is introduced. The maximum run-up height is decided from the wave’s amplitude at shoreline, and the maximum run-up distance from the shoreline is proportional to the wavelength of the edge waves. The fluid alongshore-mass-transport profile shows that the strongest mass transport rate corresponds to the position offshoreward multiplied by 0.0362 times the wavelength, and its magnitude is 1.23 times the mass-transport rate at the shoreline. The maximum cross-sectional total mass-transport rate is 0.214 times the mass transport at the shoreline, multiplied by the wavelength for the maximum run-up condition. This study suggests that edge waves cannot be increased infinitely and that there is a maximum run-up on the coast.