Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Roozbahan, M.Has files: No

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  • Article
    Citation - Scopus: 1
    Evaluating the Seismic Performance of Advanced Tuned Mass Dampers Considering Soil–Structure Interaction Effect
    (Springer Science and Business Media Deutschland GmbH, 2025) Shahraki, M.A.; Roozbahan, M.
    This study examines the seismic effectiveness of four different tuned mass damper (TMD) configurations: classical TMD, Tuned Mass Damper Inerter (TMDI), Elastoplastic Tuned Mass Damper Inerter (PTMDI), and Dual-Stiffness Tuned Mass Damper (DSTMD), focusing on their ability to reduce structural responses. A model of a 10-story steel shear frame is used, accounting for soil–structure interaction (SSI) effect to represent realistic conditions. The damper parameters are optimized using the Mouth Brooding Fish (MBF) algorithm with a hybrid objective function combining normalized peak displacement and kinetic energy reduction. The optimization process is tested against fourteen near- and far-field earthquake scenarios, with an additional ten records used to validate performance. The findings reveal that, under fixed-base conditions, TMD and TMDI achieve the largest displacement reductions (37.6% and 37.5%, respectively), while PTMDI provides the greatest kinetic energy mitigation (56.4%). DSTMD shows moderate reductions in both responses (≈ 23% displacement, 29.3% energy). When soil–structure interaction is considered, the efficiency of all systems decreases. TMDI maintains the best displacement reduction (12.9%), whereas PTMDI offers the highest energy reduction (25.5%). Additional assessments of roof acceleration and base shear highlight important trade-offs, stressing the importance of a multidimensional evaluation. In summary, this research underscores the significance of energy-based metrics and the influence of the SSI effect in evaluating dampers. Instead of advocating for or against any specific system, the analysis offers a comparative perspective on their performance under various conditions, helping to inform decisions in performance-based seismic design. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Predicting the Area Moment of Inertia of Beam and Column Using Machine Learning and Hypernetexplorer
    (Springer Science and Business Media Deutschland GmbH, 2025) Aydın, Y.; Nigdeli, S.M.; Roozbahan, M.; Bekdaş, G.; Işıkdağ, Ü.
    Beams and columns are the most important elements of steel frame structures. Damage to the beam or column can lead the structure to serious hazards and cause collapse. In the structural engineering literature, it has been observed that there is not much work for area moment of inertia estimation of beam and column. The aim of this study was to predict the area moment of inertia of beam and column using HyperNetExplorer developed by the authors. This method aims to bring innovation by optimizing artificial neural networks (ANNs). In this study, a prediction study is performed using 306 collected data on beam and column area moment of inertia. Classical ML models (linear regression (LR), decision tree regression (DTR), K neighbors regression (KNN), polynomial regression (PR), random forest regression (RFR), gradient boosting regression (GBR), histogram gradient boosting regression (HGBR)) and NAS and HyperNetExplorer were applied to predict beam and column area moment of inertia. The prediction performances were compared using different performance metrics (coefficient of determination (R2) and mean squared error (MSE)) and HyperNetExplorer developed by the authors showed the highest performance (R2 = 0.98, MSE = 246.88). Furthermore, SHapley additive explanations (SHAP) were used to explain the effects of features in the prediction models and it was observed that the most effective features for model predictions were loading on beam and length. The results show that the proposed NAS base approach and the developed tool, HyperNetExplorer, provides better performance when compared with classical ML methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.