TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7149

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Publisher: search.filters.publisher.Golden Light Publishing

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  • Article
    Ensemble Machine Learning Algorithms for Thermal Comfort Prediction in HVAC Systems of Smart Buildings
    (Golden Light Publishing, 2025) Erdem, Merve Kuru; Gokalp, Osman; Calis, Gulben
    Predicting the thermal comfort of building occupants is of paramount importance in the operation of smart buildings, providing a data-driven approach to control Heating, Ventilation, and Air Conditioning (HVAC) systems for managing occupant thermal comfort and energy use, which aligns with modern sustainability and efficiency goals. Recently, ensemble machine learning (ML)-based thermal comfort prediction models have been proposed to provide more accurate estimation of thermal comfort; however, these efforts often lack a systematic and comprehensive evaluation across a wide range of ML models within a single study. To address this gap, this study presents a systematic comparative analysis of four ensemble ML frameworks (bagging, boosting, stacking, and voting) with six basic ML algorithms (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Multilayer Perceptron, and Multinomial Na & iuml;ve Bayes) and six advanced ensemble ML algorithms (Random Forest, Rotation Forest, Extra Trees, Gradient Boosting Classifier, Histogram Gradient Boosting Classifier, and Extreme Gradient Boosting). The analysis is conducted using the widely recognized ASHRAE Global Thermal Comfort Database II, providing both 3-point and 7-point Thermal Sensation Vote (TSV) predictions. Accuracy, precision, recall and F1 metrics are used for evaluation and 10-fold cross validation is applied for further comparison. The results demonstrate the Histogram Gradient Boosting (HGB) algorithm achieved the highest F1 score (0.638) for 7-point TSV prediction whereas the Random Forest (RF) algorithm provided the highest F1 score (0.549) for 7-point TSV prediction. In practice, these findings suggest that integrating RF and HGB models into Building Management Systems or IoT-based HVAC platforms can support real-time adaptive control, helping practitioners to reduce energy use while maintaining occupant comfort.
  • Article
    Efficiency of Shear Studs Manufactured From Threaded Bars on the Punching Behavior of Flat Slabs
    (Golden Light Publishing, 2023) Saatçi, Selçuk; Saatcı, Selçuk; Yaşayanlar, Yonca
    Punching resistance in flat slab systems in reinforced concrete structures is often provided with drop panels or shear reinforcement around columns. Shear studs are effectively used in these structures as shear reinforcement. However, factory-made shear studs may not be available in all locations and small quantities for small projects. Therefore, cheap shear studs that can be manufactured from widely available materials in small quantities can be very useful in certain cases. In this study, shear studs manufactured from threaded bars, widely available in hardware stores, are used for providing punching resistance to flat slabs. Stud heads were formed with T-section nuts. Four slab specimens, two with shear studs and two without, were cast and tested under concentrated loads at their mid-point. The slabs had 2150×2150×150 mm dimensions and they were cast with two different longitudinal reinforcement ratios. Test results showed that manufactured shear studs significantly increased the load and deformation capacities of the slabs. Slabs with shear studs were able to show up to three times higher bending deformations and they were able to sustain up to 50% higher loads. The study has shown that these studs can be effectively used for punching strengthening purposes in flat plate systems or in other cases where punching resistance is needed.