CivilEng

Energy-dissipating braces are novel structural components as they not only accommodate the seismic energy demand but also enhance both the flexibility and overall earthquake resistance of the structure, preventing brittle or non-ductile behavior. The novel brace proposed in this study was developed to achieve two primary objectives: first, to restrict relative displacements at its ends by dissipating energy through U-shaped flexural plates (UFPs), and second, to provide a self-centering mechanism through the use of post-tension (PT) to ensure structural re-centering after cyclic loading. The novelty of this research lies in the experimental findings showing that post-tensioned (PT) braces exhibit a flag-shaped self-centering hysteretic response, improved initial stiffness, and reduced residual displacements by 72%, while non-PT braces behave as conventional metallic dissipators with larger residual displacements. Increasing UFP thickness from 6 to 8 mm enhances strength by 22%. Stainless steel UFPs offer superior plastic recovery, whereas regular steel UFPs dissipate ~%10 more energy through greater plasticity. Energy dissipation of the brace increases with increasing PT forces and displacement due to the PT force pulling the force–displacement curve towards high force levels. This study highlights the importance of PT force and UFP parameters in a brace configuration with self-centering and metallic dissipators such as U-shaped flexural plates.

The cement industry significantly contributes to global CO₂ emissions, making material efficiency in concrete structures a crucial sustainability goal. This study addresses the challenge of excessive cement usage in traditional concrete design by optimizing a cast-in-place concrete bench. A density-based topology optimization framework was implemented in ANSYS Mechanical and enhanced with a deep-learning surrogate model to accelerate computational performance. The optimization aimed to minimize the structural mass while satisfying serviceability and strength constraints, including limits on displacement and compressive stress under realistic public-use loading conditions. The topology optimization converged after 62 iterations, achieving a 46% reduction in mass (from 258.3 kg to 139.4 kg) while maintaining a maximum deflection below 2 mm and a maximum compressive stress of 15.5 MPa, within the allowable limit for C20/25 concrete. The deep-learning surrogate model achieved strong predictive accuracy (IoU = 0.75, Dice = 0.73) and reduced computation time by over 10⁵× compared to the full finite element optimization. The optimized geometry was reconstructed and rendered using Blender for visualization. These results highlight the potential of combining topology optimization and machine learning to reduce material use, enhance structural efficiency, and support sustainable practices in concrete construction.

This article presented a method for grading and visualising corrosion in steel pedestrian bridges using Building Information Modelling (BIM). Traditional inspection methods are often manual and subjective, which reduces their reliability and repeatability. To enhance the recording and reporting of inspection results, a five-level corrosion severity grading system was developed using matched photographic data from two inspection campaigns conducted in February 2024 and April 2025. The grades were assigned based on visual signs, including surface rust, coating damage, and flaking. A Dynamo script was used to link each grade to the corresponding elements in a Revit model using colour overrides. The proposed approach enables corrosion data to be integrated into the BIM environment in a clear, structured manner. This helps engineers assess the structure’s condition, monitor changes over time, and make informed maintenance decisions. The workflow was demonstrated using case studies from a steel pedestrian bridge in Aveiro, Portugal. The method is adaptable for future digital twin applications and supports the development of BIM-based tools for bridge asset management. The workflow was applied to over 2600 elements, with 75 visually degraded cases identified and classified into five grades, demonstrating the method’s feasibility for systematic corrosion tracking. The proposed workflow was tested on a coastal steel bridge and could be generalised to other bridges with similar environmental conditions.