Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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Subject: search.filters.subject.Artificial Neural NetworkWoS Q: search.filters.wosQuartile.N/A

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  • Conference Object
    Developing Machine Learning Models to Predict Outdoor Thermal Comfort of Kinetic Shading Devices: An Approach for Global Optimization
    (Education and Research in Computer Aided Architectural Design in Europe, 2025) Dağlier, Y.; Ekici, B.; Korkmaz, K.
    Utilizing artificial intelligence (AI) methods in the design process supports the achievement of sustainable alternatives during the conceptual design. In various AI methods, optimization and machine learning (ML) algorithms are the most common methods to develop predictive models and discover favorable design alternatives with significantly reduced computational time. Recent works focused on limited datasets, as well as the evaluation of the developed prediction models based on collected data. During the optimization process of complex design problems, the number of design parameters becomes enormous; thus, search areas contain many design alternatives that might lead the search outside of the collected data. Therefore, evaluating the accuracy of prediction models only based on the collected samples may result in scenarios where the predicted outcome during the optimization process aligns with an unrealistic solution. This study investigates how accurately prediction models developed using different ML algorithms can perform in optimization processes. The proposed framework is used to cope with outdoor thermal performance, considering kinetic shading devices with rigid origami techniques. A parametric shading device model with kinematic principles and 10 design parameters is created in Grasshopper 3d. LadyBug is used to analyze the performance of the universal thermal climate index (UTCI). To minimize the UTCI, the radial basis function optimization (RBFOpt) algorithm in the Opossum plugin is used. To compare the optimization results with the prediction results, multiple linear regression, support vector machines, random forest, polynomial regression algorithms, and artificial neural networks (ANN) are developed to predict outdoor thermal comfort performance targets on each collected data set with 2000 samples. Results showed that ANN models can provide more accurate predictions during the optimization process. The paper aims to discuss the way ML algorithms are applied and evaluated for ML-based optimization domains in design problems. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.
  • Conference Object
    Application of Artificial Neural Network for Predicting Peak Discharge From Breached Embankment Dam
    (International Association for Hydro-Environment Engineering and Research (IAHR), 2024) Okan, M.; Bor, A.; Tayfur, G.
    Estimation of peak discharge is a key parameter for risk assessment in case of dam failure, and has attracted great attention from researchers in recent years. Many formulas are available in the literature, but these cannot cover all experimental scenarios. Existing models are typically inadequate to address the complexities of dam breaches. This research attempted to predict the peak discharge in the breached embankments with an artificial neural network (ANN) model, which is effective in nonlinear problems, using datasets obtained from various dam breaches cited in the literature. The ANN model is useful in the preparation of emergency action plans since it enables prediction of peak discharge. Multilayer Perceptron (MLP) with Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms was used to predict peak discharges from breached embankments. The dataset was divided into three: 56% for training, 20% for validation and 24% for testing. Different scenarios were created using different input combinations. Performance evaluation was based on the root-mean squared error (RMSE), percent bias (PBIAS), determination of coefficient (R2), Nash-Sutcliffe efficiency (NSE) and RMSE-observations standard deviation ratio (RSR). A comparison of training algorithms revealed that LM showed the best performance when the best ANN was selected from 1000 networks. Volume of water above the breach bottom (Vw) had a greater effect on model performance than the depth of water above the breach bottom (Hw). The best performance was obtained when both Vw and Hw were used as input. © 2024 ISHS. All Rights Reserved.