Due to the impact of climate change, extreme rainfall events are becoming more frequent, resulting in shallow slope collapse and erosion that trigger debris flows. While traditional reinforcement methods like anchoring and nailing are effective, they can be costly and environmentally unfriendly. To address this issue, researchers have investigated using in situ soil reinforcement with vegetation, which is a more sustainable and economical option. In this study, a soil improvement agent was developed using leaf mold and herbal medicine to promote vegetation growth. Adding microcement and gypsum hemihydrate increased the shear strength of the soil, preventing surface erosion. A laboratory test confirmed that the combination of these ingredients effectively increased the soil’s resistance to erosion caused by rainfall. The soil improvement agent proposed in this study was applied to the case of the slope failure in the Gwangju area, South Korea, to confirm the slope stability for 10 days of rainfall. The results of numerical analysis confirmed that the reinforced slope cured by the pozzolanic reaction using the developed material improved the slope stability by 36% compared to the original soil slope during the rainy season.

There has been a great increase in the number of structural members composed of two or more materials with different stress–strain distributions. This study investigated the bending performance of composite H-shaped piles subjected to bending moments. Load–displacement hysteresis curves were analyzed using limit state analysis, and eight specimens—six composite H-shaped piles made of reinforced concrete and H-shaped steel beams (i.e., composite H-shaped piles) and two conventional H-shaped steel piles—were tested using a loading apparatus. The test results show that the ductility capacity of the composite H-shaped pile is 138 % more than that of the conventional H-shaped steel pile.

In this study the process by which a steel column subjected to strong and repeated loading undergoes failure is investigated. Furthermore, a damage index is developed by using factors related to damage along with a relevant analysis method. A cantilever-type box-type steel member, which is assumed to undergo buckling deformation, is used for the analysis. A nonlinear analysis with repeated displacement control is conducted by applying a compressive load that leads to failure along with a predetermined compressive load to the steel member. The main variables considered in this analysis are the load stock pattern and the type of steel (SS400, SM570, and Posten 80). The influence of each variable on the failure mode, deformation capacity, and damaging process is described. The failure processes for each type of steel are compared. The results indicate that the failure of a steel member under strong repeated loading is governed by local buckling and that the failure of a steel member is directly related to the local strain.

Following an investigation undertaken by the publisher, this paper has been found to be in violation of ASTM’s Plagiarism Policy. We hereby retract this paper. Reasonable effort should be made to remove references to this paper. The corresponding author, as the representative of all authors, has been notified of the retraction, and we have kept a record of any responses received. Due to the nature of this duplicate publication, the full article has been removed by ASTM International in July 2025. Retraction Statement: https://doi.org/10.1520/JTE20259999

This study proposes a method for monitoring traffic conditions via the collection of road traffic data using drones and deep learning-based analysis. Utilizing the YOLOv8 object detection model, we analyzed traffic videos captured at 45 and 90 degrees at intersections, which revealed differences in object detection capabilities based on the shooting angle. To address issues related to occluded areas during object tracking, we optimized the shooting angles, and achieved a final model with an average of 96.9% and 93.7% path and vehicle type recognition rate, respectively. This validates the effective utilization of drone-based traffic data collection and deep learning analysis in road traffic management. Further research is necessary for the development and application of more sophisticated traffic information systems in the future.

Hsu recently conducted a shear test on nine reinforced concrete panel elements subjected to applying pure shear using a shear testing device. Modern truss models (i.e. modified compression field theory and a rotating angle softened truss model) are used to perform a complex nonlinear analysis through a trial and error method based on a double loop. This analysis is conducted by employing equilibrium conditions, compatibility conditions, and a ductile stress–strain relationship of a reinforced concrete membrane panel in a biaxial state. In this study, an effective algorithm that uses a revised Mohr compatibility method based on the failure criteria of struts and ties is proposed. This algorithm is used to improve the convergence rate in the analysis of shear history, which was performed in the experiment of Hsu. The result of the analysis indicates that the shear strain energy in a state of extended shear strain is influenced by the relationship between principal compressive stress and strain (crushing failure).