This study proposes a wind power generation system designed to enhance energy self-sufficiency in high-rise buildings by integrating internal and external airflow sources. In such structures, external wind generated by building height and form and internal airflow induced by the stack effect coexist but move in different directions. This study introduces a system that collects and integrates five types of wind into a single, unified flow. Wind speed data were calculated using meteorological datasets and stack effect equations, and a prototype was designed using Autodesk Fusion360. CFD simulations were conducted to determine the combined wind speed at the outlet. Based on the simulated results, expected power output was calculated using wind power equations. The building energy model was created in DesignBuilder and simulated in EnergyPlus using default office building settings. The predicted annual wind energy generation is approximately 962,022.85kWh, covering 11.73% of the building’s total annual electricity use. The study demonstrates the potential for wind-integrated high-rise designs to contribute to energy autonomy.
This study investigates the seismic fragility of nuclear power plant (NPP) auxiliary structures by incorporating material aging deterioration into machine learning–based response prediction models. An artificial neural network (ANN) was developed using 17 seismic and material parameters, achieving high predictive accuracy (R2 = 0.96) while significantly reducing computational demands compared with conventional finite element analyses. By combining the ANN with Monte Carlo simulations, fragility curves for Motor Control Center (MCC) cabinet anchors were derived at resonance frequencies of 10 Hz and 15 Hz. Results indicate that equipment with higher resonance frequency (15 Hz) exhibits lower seismic vulnerability due to reduced sensitivity to dominant low-frequency seismic components. When material deterioration was introduced, fragility curves shifted toward lower ground motion intensities, reflecting increased failure probabilities and approximately 20% reduction in median seismic capacity. These findings highlight the necessity of considering aging effects in probabilistic seismic risk assessments and demonstrate the efficiency of ML-based surrogate models for quantifying long-term safety margins of NPP structures.
In this study, a new Lattice integrated Rib-Type Deck Plate system is proposed to enhance the flexural stiffness and strength. This system integrated a rib-type corrugated deck with a steel wire lattice designed for short spans, which is placed on top of the corrugated deck. To validate the flexural performance of the proposed system, experimental programs were conducted for both the construction stage and the composite stage. Construction stage test results showed that all specimens exhibited deflections less than 13mm under the construction load, satisfying the maximum deflection limit of 19mm. Composite stage test results indicated that increasing the amount of tension and compression reinforcement led to improvements in initial stiffness, yield strength, and maximum strength. Furthermore, specimens with reinforcement in the compression zone demonstrated superior ductility. The flexural strength of all tested specimens was confirmed to be safely evaluated by the flexural strength equation specified in the Korea Design Standard.
This paper proposes an Adjustable Circular Steel Connector(ACS) to enable immediate response to construction errors that may occur during the on-site assembly of modular members. To evaluate its flexural performance, one rectangular steel tube beam specimen without ACS and three specimens with ACS(with varying ACS lengths) were fabricated. Experimental results showed no pull-out occurred between Shank A and Shank B, and the application of ACS facilitated stress distribution in the rectangular steel tube. The specimens with ACS exhibited up to 92% of the maximum load capacity of the monolithic specimen, and showed increasingly ductile behavior as the ACS length increased. Due to the cross-sectional difference between Shank A and Shank B within the ACS, yielding was observed in Shank B. Further studies are needed on this part, as well as on the behavior of the ACS under cyclic loading conditions.
This study develops and evaluates a prompt-driven large language model (LLM) agent for section design of doubly reinforced concrete (RC) beams. Using Google Gemini (Gems), an engineering “expert” that operates without fine-tuning by uploading ACI-318 provisions, sample design documents, and a database of prior beam designs was developed. The agent interprets code clauses, formulas, and constraints from these materials and retrieves similar design cases to propose an initial solution. It then incorporates user-specified natural-language constraints—most notably a strength-ratio cap (design strength ≤ 105% of required strength)—to iteratively refine toward safe and economical designs. Beyond reporting member size and reinforcement details, the agent provides step-by-step computational justifications for moment and shear checks, increasing verifiability and instructional value. We benchmark the LLM-generated designs against results from the commercial program MIDAS/Design+ and observe close agreement. In several scenarios, the constraint-guided LLM solutions are more material-efficient while remaining code-compliant. The workflow also supports batch processing from spreadsheet inputs, enabling practical automation across multiple beams. The approach requires no additional model training or coding making it accessible to non-developer practitioners. Results indicate that a general-purpose LLM, properly grounded with code documents and examples, can achieve practice-level performance with transparent reasoning. This demonstrates a viable approach to AI-assisted structural design that is explainable, interactive, and readily integrated with engineering workflows.
In reinforced concrete (RC) structures, steel corrosion can be occurred due to carbonation and chloride penetration, and these phenomenon lead to a degradation of the structural performance. Existing analytical models for corroded RC columns depend on complex empirical formulas based on specific experimental data. This study proposes a macro analytical model to predict the lateral load-resisting behavior of corroded RC columns. The proposed model is composed of a force-based nonlinear beam–column element that simulates the flexural behavior and a zero-length section element that represents the bond–slip deformation at the column base. To validate the proposed model, its results were compared and analyzed with the experimental results from existing literature. The results showed that the proposed model evaluated the maximum strength and the residual strength at a 4% drift ratio similarly to the experimental values. Furthermore, the model effectively predicted the pinching phenomenon and the hysteretic behavior under cyclic loading.
Reinforced concrete structures require effective strengthening methods to improve shear capacity and ductility. Conventional external systems such as steel plates or CFRP sheets are limited by premature debonding and member damage. This study experimentally evaluated the shear performance of concrete beams strengthened with iron-based shape memory alloy (Fe-SMA) strips. Static loading tests compared the effects of prestressing activation, retrofit type and retrofit ratio. The activation of Fe-SMA effectively delayed the formation of shear cracks and reduced width. Also, the Fe-SMA suppressed the shear deformation of stirrups and concrete, resulting in enhanced shear performance and ductility of the strengthened beams. Overall, the Fe-SMA strengthening method was found to be effective in improving the serviceability and maintenance performance of reinforced concrete beams.
Rapid post-earthquake retrofit decisions require reliable estimates of interstory drift ratio. Conventional field practices either depend on instrumented measurements constrained by sparse sensor coverage or rely on qualitative expert judgment. This study aims to develop a CNN-based interstory drift ratio prediction method for reinforced concrete columns using strain-derived damage images. Reinforced concrete columns are modeled and analyzed in OpenSees to obtain strains and displacements. Strain fields are converted into strain-derived damage images through threshold-based staging that encodes discrete damage states. Structural parameters are concatenated to the damage image by adding fixed-value columns so the network can read structural context in a single two-dimensional input. We design systematic comparisons to isolate the benefit of structural information and section coverage. First, models without structural parameters are trained. Second, single-parameter variants are trained where only one attribute is provided. Third, full-parameter models include all attributes. For each setting, both single-section and multi-section inputs are evaluated. Samples are split by case and then divided 80/20 into training and validation sets. Model performance is reported using RMSE, MAE, and R-squared. The proposed approach achieves accurate inter-story drift ratio prediction overall, with improved performance when all structural parameters and multi-section inputs are used.
This study experimentally evaluated the flexural behavior of reinforced concrete (RC) beams incorporating a high-performance cementitious composite (VC) with 1.0 vol.% Vectran fibers. Three-point bending tests were conducted on a reference high-strength concrete beam (RCB) and two VC beams (VCB-1, VCB-2). Compared with RCB, the maximum load increased by +19.8% (VCB-1) and +9.0% (VCB-2), while the yield load rose by +18.9% and +16.0%, respectively. The ductility index (Δu/Δy) improved from 1.89 (RCB) to 5.22 (VCB-1), confirming the crack control effect based on multiple micro-cracking. The improved performance indicates not only enhanced flexural capacity and ductility but also suggests the potential for carbon-neutral structural design through material reduction and service-life extension enabled by the Vectran fiber-reinforced composite system.
Reviving culture is not limited to preservation or reconstruction; it requires revealing its values and engaging the public, thereby establishing communication between the past and the present. A decade has passed since the fire at Sungnyemun, and citizens’ memories of the event have gradually faded. Although the gate has been restored to its place within the daily fabric of the city, its painful history is slowly being forgotten. This study seeks an architectural approach that reconnects citizens with Sungnyemun through spatial narrative and feng shui interpretation, emphasizing the historical and geomantic meanings of the site. Here, place is defined not as a physical environment but as a living space composed of human behavior, relationships, and accumulated time—an intersection of history and memory. Recognizing indifference as a key cause of the fire, this research proposes a memorial space as a medium to reawaken public awareness and revive the cultural value of Sungnyemun, enabling citizens to reflect upon history through collective experience.