Browsing by Author "Özyüksel Çiftçioğlu A."
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Item Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic(Elsevier Ltd, 2021) Khalilpourazari S.; Hashemi Doulabi H.; Özyüksel Çiftçioğlu A.; Weber G.-W.This research proposes a new type of Grey Wolf optimizer named Gradient-based Grey Wolf Optimizer (GGWO). Using gradient information, we accelerated the convergence of the algorithm that enables us to solve well-known complex benchmark functions optimally for the first time in this field. We also used the Gaussian walk and Lévy flight to improve the exploration and exploitation capabilities of the GGWO to avoid trapping in local optima. We apply the suggested method to several benchmark functions to show its efficiency. The outcomes reveal that our algorithm performs superior to most existing algorithms in the literature in most benchmarks. Moreover, we apply our algorithm for predicting the COVID-19 pandemic in the US. Since the prediction of the epidemic is a complicated task due to its stochastic nature, presenting efficient methods to solve the problem is vital. Since the healthcare system has a limited capacity, it is essential to predict the pandemic's future trend to avoid overload. Our results predict that the US will have almost 16 million cases by the end of November. The upcoming peak in the number of infected, ICU admitted cases would be mid-to-end November. In the end, we proposed several managerial insights that will help the policymakers have a clearer vision about the growth of COVID-19 and avoid equipment shortages in healthcare systems. © 2021 Elsevier LtdItem Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence(Springer, 2021) Khalilpourazari S.; Khalilpourazary S.; Özyüksel Çiftçioğlu A.; Weber G.-W.This paper suggests a novel robust formulation designed for optimizing the parameters of the turning process in an uncertain environment for the first time. The aim is to achieve the lowest energy consumption and highest precision. With this aim, the current paper considers uncertain parameters, objective functions, and constraints in the offered mathematical model. We proposed several uncertain models and validated the results in real-world case studies. In addition, several artificial intelligence-based solution techniques are designed to solve the complex nonlinear problem. We determined the most efficient solution approach by solving various test problems. Then, simulated several scenarios to demonstrate the robustness of our results. The results showed that the solutions provided by the offered model significantly reduce energy consumption in different setups. To ensure the reliability of the results, we carried out worst-case sensitivity analyses and found the most critical parameters. The results of the worst-case analyses indicated that the offered robust model is efficient and saves a significant amount of energy comparing to traditional models. It is shown that the provided solution by the presented robust formulation is reliable in all situations and results in the lowest energy and the best machining precision. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Item Hiding in plain sight: What can interpretable unsupervised machine learning and clustering analysis tell us about the fire behavior of reinforced concrete columns?(Elsevier Ltd, 2022) Özyüksel Çiftçioğlu A.; Naser M.Z.The role of machine learning (ML) continues to rise in the structural fire engineering area. Noting the widespread of supervised ML approaches, such methods are being heavily utilized nowadays. On the other hand, little interest has been dedicated to unsupervised ML. Unlike supervised learning, unsupervised learning algorithms are trained using data that is neither classified nor labeled, thus, allowing the algorithm, and without guidance, to identify unseen patterns and decode hidden structures of complex phenomena residing in data. In this pursuit, this study presents findings obtained from one of the earliest investigations aimed to explore the potential of unsupervised and interpretable machine learning (ML) clustering analysis to investigate the response of reinforced concrete (RC) columns under fire conditions. We used four algorithms (namely, K-Means, Hierarchical, Fuzzy C-Means, and DBSCAN) to cluster over 140 fire-exposed RC columns. Results from our clustering analysis show that the performance of such columns can be distinctly grouped into unique clusters. Our findings allow structural fire engineers to 1) identify and avoid RC columns with poor fire performance and, when possible, 2) upgrade the features/characteristics of columns of poor fire response to achieve an improved behavior. Overall, our analysis indicates that fire-exposed RC columns naturally fit into four clusters – each with unique properties and response – that are governed by the material, geometric and loading features. © 2022 Institution of Structural EngineersItem Estimation of Natural Frequencies of Pipe–Fluid–Mass System by Using Causal Discovery Algorithm(Institute for Ionics, 2023) Dagli B.Y.; Ergut A.; Özyüksel Çiftçioğlu A.This paper employs a novel approach to investigating the dynamic behavior of a pipe conveying fluid and the relationship between the variables that influence it, based on causal inference. The pipe is modeled as a beam with Rayleigh beam theory and Hamilton’s variation principle is demonstrated to obtain the equation of motion. Concentrated mass at various locations is introduced using the Dirac delta function. The fluid in the pipe has no compression properties and no viscosity. The non-dimensional equations of motion of the pipe–fluid–mass system are achieved by using the approach of the fluid–structure interaction problem. The non-dimensional partial differential equations of motion are converted into matrix equations and the values of natural frequencies are obtained by using the Finite Differences Method. The relationship between the variables is investigated by causal discovery using the produced natural vibration frequencies dataset. Moreover, the Bayesian Network's probability distribution is fitted to the discretized data using the structural model created through causal discovery, resulting in trustworthy predictions without the need for sophisticated analysis. The findings highlighted that the proposed causal discovery can be an alternative practical way for real-time applications of pipe conveying fluid systems. © 2022, King Fahd University of Petroleum & Minerals.Item RAGN-L: A stacked ensemble learning technique for classification of Fire-Resistant columns(Elsevier Ltd, 2024) Özyüksel Çiftçioğlu A.One of the main challenges in using reinforced concrete materials in structures is to comprehend their fire resistance. The assessment of fire resistance can be performed in a laboratory environment using fire. However, such tests are time-consuming and expensive, and they may not provide a complete assessment of all relevant properties of a particular tested specimen. To that end, the implementation of machine learning (ML) in the investigation of fire-resistant structural performance would be beneficial, as it would also contribute to the reduction of time and cost problems related to traditional techniques. Here, this research proposes a novel ensemble ML approach to classify columns according to their fire resistance characteristics, supporting the application of ML techniques by fire engineers and scientists. The proposed model, named RAGN-L, combines Random Forest, Adaptive Boosting, and Gradient Naive Bayes, and is stacked using the Logistic Regression approach. RAGN-L is evaluated on real-world databases of reinforced concrete columns and concrete-filled steel tube columns, as well as a synthetic database generated by the TVAE deep learning model. The performance of the proposed solution is compared with ten different ML classifiers based on common statistical metrics, accuracy, precision, recall, and f1-score, and validated using the k-fold cross-validation approach. The developed algorithm outperforms ten different classifiers in all databases, with classification accuracies of 86.6%, and 99.6% for the real-world and synthetic databases of reinforced concrete columns, respectively, and 88.1% for the real-world database of concrete-filled steel tube columns. © 2023 Elsevier LtdItem Enhancing Structural Evaluation: Machine Learning Approaches for Inadequate Reinforced Concrete Frames(Springer Science and Business Media Deutschland GmbH, 2024) Altıok T.Y.; Üstüner B.; Özyüksel Çiftçioğlu A.; Demir A.This paper provides comprehensive analyses of the performance of inadequate reinforced concrete frames from different aspects. First, a one-story, single-span, 1/3 scaled frame is constructed. The ultimate lateral load-bearing capacity of the sample is experimentally determined. Second, the behavior of the sample is obtained by finite element analysis. In the analyses, axial load, rebar diameter, and concrete strength values are taken as variables. Load–displacement behaviors determined from the tests and finite element analyses are compared. Third, machine learning models are developed to estimate the ultimate load-bearing capacity of the frames. Random Forest, ElasticNet, RANSAC, Decision Tree, K-Nearest Neighbors, and Gaussian Naive Bayes are employed to assess the load-bearing capacities of the frames. Coefficient of determination, Root Mean Squared Error, Mean Absolute Error, and Mean Absolute Percentage Error, which are widely recognized performance indicators, are also employed to assess the effectiveness of machine learning methods. The findings reveal that the Random Forest method is the most precise and effective in both regression and classification analyses. It has the highest Coefficient of determination of 87% in predicting the load-bearing capacity of the frame and the highest accuracy of 100% in classifying the frames based on their load-bearing capacity. This is the first attempt to employ machine learning approaches to assess the load-bearing capacity of inadequate reinforced concrete frames. The proposed model can provide a better understanding of inadequate frames and has advantages over other analysis methods with respect to simplicity in application, flexibility, reducing the time and cost associated with the process. © The Author(s), under exclusive licence to Shiraz University 2024.