Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
One of the most critical aspects of design for an analyst or designer is understanding the service loads that a system or component will experience. In a standard finite element (FE) analysis, the service load history is applied to the FE model to generate the corresponding history of stresses and strains, which are necessary for further evaluations. However, for components operating in complex environments, accurately measuring or predicting the service load history can be particularly challenging. Instrumenting a prototype with load transducers is often an expensive and time-consuming process and, most importantly, may physically alter the component, changing its mass, stiffness, and load path, causing discrepancies between the measured and actual loads. In this context, this paper presents a load identification method, enhancing the methodology behind the load identification theory and reducing the uncertainties inherent in the standard approach, primarily due to the placement, number, and orientation of strain gauges....
Cities worldwide face profound morphological changes due to population growth and urban densification. Coupled with climate change, this exacerbates the Urban Heat Island (UHI) effect and degrades outdoor thermal comfort. This paper introduces a novel simulation framework for climate-resilient urban design, transitioning from static planning standards to dynamic performance optimization. This research utilizes a multi-tiered data acquisition strategy, beginning with a PRISMA-guided Systematic Literature Review of 133 articles to identify key UHI mitigation variables. A high-fidelity, multi-physics Computational Fluid Dynamics (CFD) model was developed using the ANSYS Fluent solver, discretized with a poly-hexacore mesh of over 78 million cells. The simulation environment integrates multiscale data, including 2.5D urban geometry from GIS platforms, high-resolution satellite information (e.g., Copernicus and LiDAR) for surface and soil properties, and EUMETSAT weather files for boundary conditions. The model explicitly resolves aerodynamic and thermodynamic exchanges using Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations, with vegetation represented via porous-medium parameterization. The core novelty lies in the development of a parameterized library of “Architectural Elements” (AEs) that introduces standardized material properties, derived from Ansys Granta Selector, directly with GIS-based street designs. This allows for iterative “what-if” scenario analyses over critical 24 h periods to assess the synergistic impact of green infrastructure (GI) and advanced materials. Validation against real-world monitoring data from the Grow-Green project confirmed the model’s accuracy, with a maximum error of only 0.22%. The results demonstrate that interconnecting isolated green areas and utilizing local porous materials can reduce UHI spot temperatures by 2–4 ◦C while significantly lowering building energy consumption....
The Compact Linear Collider (CLIC) main linac employs a waveguide-damped structure as its baseline design. To ensure beam dynamic stability, the transverse wakefield must be less than 3.4 V/pC/m/mm in 0.5 ns, corresponding with the position of the second bunch. This study focuses on optimizing high-order-mode (HOM) damping loads through new material measurements, achieving enhanced suppression of long-range transverse wakefields. The new damping load is 20% more compact than those in the previous CLIC structure design....
This study investigates seismic loads in single-story masonry buildings with walls of varying heights and thicknesses, and determines optimum wall dimensions for seismic resistance using the Taguchi method. For this purpose, 25 (5 × 5 = 25) different masonry building models were created with thicknesses of 16, 20, 24, 28, and 32 cm and heights of 260, 280, 300, 320, and 340 cm. The building models were analysed using a software package in accordance with the 2018 Turkish Building Earthquake Code (2018 TBEC). C-30 concrete and S-420 steel were used in the designed building models. A 12 cm thick reinforced concrete slab was placed on top of the masonry walls. A live load of 0.2 t/m2 was designed on the slab, and the mortar strength of the brick wall was taken as 30 MPa. When a building model with a height of 260 cm and a thickness of 16 cm was used as a reference, it was observed that the seismic resistance of other building models increased by approximately 72%, while shear forces increased by approximately 89% in the “x” direction and approximately 95% in the “y” direction. Furthermore, it was observed that as the ratio of wall height to wall thickness increased, the seismic resistance of the building models decreased. The seismic resistance of 25 dierent building models was analysed using the Taguchi method, depending on wall thickness and wall height. The analysis revealed that the building model with walls 24 cm thick and 340 cm high was the most resistant to shear forces, while the building model with walls 32 cm thick and 340 cm high provided the best resistance to seismic loads....
This paper aims to present a non-destructive, optimized variational mode decomposition (VMD)-based ground-penetrating radar (GPR) method developed for identifying void defects in reinforced concrete (RC) structures. This study also presents an enhanced framework for defect detection in RC by integrating advanced spectrum analysis with deep learning techniques. A GPR investigation was conducted on an RC bridge deck with known structural defects to generate a representative dataset reflecting both intact and void-defective conditions. In addition to conventional spectral techniques such as fast Fourier transform (FFT), spectrogram, and scalogram, an optimized variational mode decomposition (VMD) method was implemented. The VMD approach decomposes GPR signals into intrinsic mode functions, enabling refined feature extraction beyond traditional spectral methods and allowing clear differentiation between intact and defective signals. The limited availability and quality of GPR small datasets have restricted the application of a functional 1D-CNN which generally requires at least several hundred datasets. To address this challenge, a data augmentation strategy is adopted. FFT-based features were successfully utilized to train a one-dimensional convolutional neural network (1D-CNN) for automated defect identification. The results demonstrate that both the advanced spectrum-based approach and the hybrid framework combining spectral analysis with deep learning significantly improve defect detection performance. Overall, the proposed methodology provides an effective and intelligent solution to support timely, data-driven decision-making for maintenance and safety assurance of bridge infrastructure....
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