Browsing by Author "Ciftcioglu A.O."

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    Optimum design of cellular beams via bat algorithm with levy flights
    (CEUR-WS, 2017) Dogan E.; Ciftcioglu A.O.; Erdal F.
    Recently, several non-deterministic search techniques have been proposed for the development of structural optimization problems. This study presents a bat algorithm for the optimum solution of engineering optimization problems. Bat algorithm is based on the micro-bats' echolocation capability. They use echo sounder to identify prey, keep away from obstacles (barriers) and settle their roosting crevices in the darkness. Bats give out a very powerful sound and then listen its echo from the nearby items. They even use the time retard from the emission and sensing of the echo. They can notice the distance and position of the target, target's characteristics and even the target's moving speed such as very small insects. Bat algorithm is an optimum design algorithm for the automatization of optimum design process, during which the design variables are chosen for the minimum objective function value limited by the design constraints. Three varied cellular beam problems subjected different loading are selected as numerical design examples. Also in this study, Levy Flights is adapted to the simple bat algorithm for better solution. For comparison, three cellular beam problems solved for the optimum solution by using bat algorithm and bat algorithm with Levy Flights technique. Results bring out that bat algorithm is effective in finding the optimum solution for each design problem. Moreover, adaptation of Levy Flights technique to simple bat algorithm generates better solutions than the solutions obtained by simple bat algorithm. © Copyright by the paper's authors.
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    Machine learning approach to predict the mechanical properties of cementitious materials containing carbon nanotubes
    (Elsevier Ltd, 2024) Okasha N.M.; Mirrashid M.; Naderpour H.; Ciftcioglu A.O.; Meddage D.P.P.; Ezami N.
    This research explores the use of machine learning to predict the mechanical properties of cementitious materials enhanced with carbon nanotubes (CNTs). Specifically, the study focuses on estimating the elastic modulus and flexural strength of these novel composite materials, with the potential to significantly impact the construction industry. Seven key variables were analyzed including water-to-cement ratio, sand-to-cement ratio, curing age, CNT aspect ratio, CNT content, surfactant-to-CNT ratio, and sonication time. Artificial neural network, support vector regression, and histogram gradient boosting, were used to predict these mechanical properties. Furthermore, a user-friendly formula was extracted from the neural network model. Each model performance was evaluated, revealing the neural network to be the most effective for predicting the elastic modulus. However, the histogram gradient boosting model outperformed all others in predicting flexural strength. These findings highlight the effectiveness of the employed techniques, in accurately predicting the properties of CNT-enhanced cementitious materials. Additionally, extracting formulas from the neural network provides valuable insights into the interplay between input parameters and mechanical properties. © 2024 The Authors