In this study, the use of simple attachments to existing guardrail members was investigated to evaluate their potential for reducing the overall drag force on long-span bridge decks. Wind tunnel testing and computational fluid dynamics (CFD) analysis were conducted to reveal the drag force reduction effect with the placement of attachments onto the rear sides of guardrails. A series of case studies of single-rail members and three-layer guardrails was performed to determine the optimal shape. A steel twin-box suspension bridge with a main span of 1545 m was selected as a prototype bridge deck, and the evaluation indicated an overall drag reduction ratio of 11.1% when an optimal attachment was applied. CFD analysis was also conducted to clarify the mechanisms responsible for the drag reduction achieved by the modified guardrails and to address Reynolds number effects. This study demonstrates the importance of optimizing the aerodynamic shape of guardrails and achieving drag force reduction by adding attachments to the rear sides of the guardrails without compromising the crash performance.

The creep effect becomes significantly pronounced when a steel structure has prolonged exposure to fire. To investigate the influence of creep on displacement, stress, and fire resistance time of spatial grid structure subjected to fire, a square pyramid spatial grid structure was taken as the research object. The element tangent stiffness matrix considering creep effect was derived, and a series of numerical simulation models were established with two constitutive models of materials, a creep model and an elastoplastic model. A comparative analysis of whether to consider creep effects was performed, aiming to assess the differences in the structural response and fire resistance duration of the spatial grid structure subjected to fire. The analysis indicates that the creep effects on both displacement and stress can be disregarded for structural fire durations under 0.5 h, however, once the fire duration exceeds 0.5 h, structural deformation increases significantly, especially when the creep is considered, and the stresses in members decreased significantly, therefore, the influence of creep must be considered in the fire analysis of spatial grid structures to avoid unsafe predictions. The research results aim to provide valuable references for the analysis of spatial grid structure fire responses and post-fire assessments of structures.

Designing earthquake-resistant buildings requires an accurate computation of the fundamental period. Therefore, it is crucial to estimate it through a probabilistic approach. This paper aims to forecast the period of infilled RC structures using a probabilistic method, taking into account the uncertainties in the multivariate adaptive regression splines equation. Sensitivity analysis revealed that building height and its interaction with opening percentage have the most significant impact on period estimation, whereas span numbers have no significant effect. Moreover, the output statistical analysis of the Monte Carlo simulation-generated data revealed that the multivariate distribution fits all features. The 95% confidence interval demonstrated that the period standard deviation is more sensitive to feature variations than the mean. The uncertainty in the variation of span length and masonry stiffness had negligible effects on the mean period, while the opening percentage had the highest impact on period statistics. Furthermore, two expressions for period prediction with three and four features were developed, achieving an accuracy of 97% and 99%, respectively. The 95% prediction interval showed that most data points fell within the predicted intervals, illustrating low uncertainty in the developed formulas, and comparing them with the authors’ and building code formulas highlighted their superiority by achieving the lowest errors.

Historic buildings are clear testaments to human civilization, and their preservation is essential for the world's cultural heritage. This study examines a unique heritage building in Turkiye that represents a brief period of Russian rule and its architectural style. The structure, the historic Katharina Hunting Mansion, was constructed by the Russians in the Sarıkamış district of Kars Province following the 1877–1878 Ottoman-Russian War. The mansion features a unique Scandinavian saddle notch (known as 'v chashu' in Russian and 'çantı system' in the Black Sea region). A survey was conducted to determine the deformations of the building, which included the preparation of plans, facades, and sections. The architectural investigation revealed that yellow pine, a common species in the region's forests, was used as the construction material. Architectural restoration proposals were developed based on the results of seismic analyses and the classification of architectural elements as either later additions or missing components. A unique methodology that combines various seismic analysis methods in accordance with the applicable local seismic codes was performed for the seismic performance assessment. For this purpose, the finite element model of the historic mansion was subjected to linear, nonlinear, and kinematic analyses. In the analyses, both the local seismic code TBEC-2018 and the SRMGHS-2017 guideline were considered, due to the historical status and structural system of the building. This process determined whether the structure meets the Limited Damage and Controlled Damage performance objectives for DD-3 and DD-2 earthquake levels, as envisaged in the local seismic codes. The linear analysis revealed that the building met the seismic performance objective with respect to inter-story drifts. Furthermore, the results of the nonlinear pushover analysis also indicated that the building satisfied performance objectives regarding shear forces and corresponding displacements. Moreover, the kinematic analysis determined that all local collapse mechanisms occurred at DD-2 and DD-3 earthquake levels, except for the overturning mechanism under DD-2. Therefore, the restoration proposals recommended using clamping at the wall joints to prevent potential local collapse. This study contributes to the field by presenting a comprehensive methodology for the seismic assessment of historic masonry-timber buildings. Additionally, the seismic performance of this historic building, featuring a unique combination of masonry and timber elements, was found to be generally satisfactory and compliant with current local codes.

Vortex-induced vibration (VIV) is a typical nonlinear fluid–structure interaction phenomenon. Although the semi-empirical model is a theoretical and efficient manner, it is difficult to accurately predict the above nonlinear phenomena due to the incomplete mathematical expressions and uncertain parameters. In this article, the recursive radial basis function neural network (RRBFNN) is proposed to modify the typical vortex induced force (VIF) model. The RRBFNN VIF model was first constructed, and the dynamic equation for calculating VIV response was created, based on the significant nonlinear fluid–structure interaction characteristics of VIV. The aerodynamic forces of the two-edge box girders under various reduced velocities and vibration amplitudes were calculated via computational fluid dynamics (CFD) in order to obtain the mentioned VIF model parameters and verify the efficacy of several RRBFNN learning methods. Ultimately, the amplitude of vortex-induced vibration was predicted using the earlier-mentioned effective training method, and the aerodynamic decoupling method was used to investigate the influence of multiple order aerodynamic forces on the vortex-induced vibration response. As a result, the RRBFNN accurately simulates the aerodynamic force in the time domain and frequency domain. The orthogonal least squares (OLS) and exponential basis function are well-suited to the RRBFNN VIF model. The decisive factor of VIV is the fundamental frequency signal in the aerodynamic force. The second harmonic wave can significantly affect the VIV amplitude, while the influence of the third harmonic wave can be ignored.

The HiTAB (Hybrid Truss and Box) girder system is a steel composite girder bridge that combines truss structures in high-depth near continuous supports with steel box girders in shallower span regions. It integrates steel box sections optimised for flexural resistance with truss sections designed for axial-force resistance into a single continuous girder system, representing a unique bridge configuration. The Godeok-Topyeong Bridge connects Godeok-dong in Seoul and Topyeong-dong in Guri as part of the Anseong–Guri section of Expressway Route 29 (Sejong–Pocheon Expressway). While the main span adopts a concrete cable-stayed configuration, the approach span at the starting end of the bridge near the Godeok Interchange was constructed using the HiTAB girder system. This paper focuses on the 205 m (50 + 80 + 75 m) approach segment, with separate northbound/southbound structures each using three main girders. The truss–box junction adopts an overlapping configuration for smooth force transfer; box-type crossbeams at the overlap enhance local stiffness considering interaction with stringers. All girders were shop-fabricated as transport-compliant segments and field-assembled using only high-strength bolts. During erection, box and truss blocks—including the hybrid overlap—were lifted separately and bolted mid-air without temporary bents, demonstrating efficient constructability.

This paper investigates the application of Artificial Neural Networks (ANNs) for structural data augmentation, using welded beam-to-column steel joints as a representative case study within a broader research effort aimed at improving the reliability-based design of ductile steel joints. While simplified design methods such as the component method are widely adopted, they often underestimate joint resistance in ductile failure scenarios, limiting the exploitation of the full resistance and deformation capacity of steel joints. To develop improved resistance criteria and satisfy the target reliability levels defined in EN 1990, a large volume of high-fidelity structural data is required, making traditional experimental and finite element (FE) approaches computationally unfeasible for Monte Carlo simulations. As a preliminary step, this study evaluates the performance of ANNs trained on deterministic (nominal) input data derived from a validated FEM database of 2,400 welded joint configurations, enabling efficient prediction of initial stiffness and moment resistance. A second investigation then assesses the ANN's capacity to generalize when applied to stochastically varied input parameters, as required for probabilistic applications. Results show that deterministic-trained models underperform in this setting, underscoring the need for tailored training strategies. While this paper establishes a robust ANN-based methodology for deterministic modeling, it also sets the stage for future work on stochastic ANN training and reliability-based joint assessment.