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Öğe Blockchain in aviation: Applications of blockchain technology in aircraft maintenance management(CRC Press, 2024) Cakiroglu, CelalBlockchain technology emerges as a solution for fostering transparency and security within the supply chain, effectively recording and sharing maintenance data to prevent unauthorized modifications. Complementing this, digital twins offer virtual replicas of aircraft components, allowing real-time monitoring and predictive analysis to optimize maintenance schedules and minimize downtime. Streamlined aircraft spare part management through advanced inventory systems and data analytics ensure operational continuity and cost-efficiency. Predictive maintenance techniques demonstrate their prowess in forecasting equipment failures, enhancing fleet reliability while reducing maintenance costs. The current study emphasizes the incorporation of sensor technologies and AI-driven systems for continuous structural health monitoring and risk mitigation. Following a brief historical background on the evolution of the blockchain systems, an introduction to the applications of the blockchain technology and artificial intelligence in the aircraft parts maintenance sector has been provided. The key concepts of machine learning have been presented with a theoretical introduction. © 2025 selection and editorial matter, Turan Paksoy and Sercan Demir. All rights reserved.Öğe CO2 Emission Minimization of a Plate Girder Under Lateral Torsional Buckling Constraint(American Institute of Physics Inc., 2023) Cakiroglu, Celal; Bekdaş, GebrailMetaheuristic optimization algorithms are increasingly applied to the solution of engineering problems. The current study focuses on the minimization of the CO2 emission associated with the production of a plate girder. Considering the tendency to lateral-torsional buckling in these structures, the critical buckling moment has been used as one of the optimization constraints. Furthermore, the flanges and the web of the plate girder are kept within the non-compact range throughout the optimization process. As the method of optimization, the metaheuristic Jaya algorithm has been modified using Lévy probability distributions for each design variable. The flange width and thickness, the web thickness and the clear distance between the flanges are chosen to be the variables of optimization. © 2023 American Institute of Physics Inc.. All rights reserved.Öğe CO2 Emission Optimization of Concrete-Filled Steel Tubular Rectangular Stub Columns Using Metaheuristic Algorithms(Mdpi, 2021) Cakiroglu, Celal; Islam, Kamrul; Bekdas, Gebrail; Kim, Sanghun; Geem, Zong WooConcrete-filled steel tubular (CFST) columns have been assiduously investigated experimentally and numerically due to the superior structural performance they exhibit. To obtain the best possible performance from CFST columns while reducing the environmental impact, the use of optimization algorithms is indispensable. Metaheuristic optimization techniques provide the designers of CFST members with a very efficient set of tools to obtain design combinations that perform well under external loading and have a low carbon footprint at the same time. That is why metaheuristic algorithms are more applicable in civil engineering due to their high efficiency. A large number of formulas for the prediction of the axial ultimate load-carrying capacity Nu of CFST columns are available in design codes. However, a limitation of the usage of these design formulas is that most of these formulas are only applicable for narrow ranges of design variables. In this study a newly developed set of equations with a wide range of applicability that calculates Nu in case of rectangular cross-sections is applied. In order to optimize the cross-sectional dimensions, two different metaheuristic algorithms are used, and their performances are compared. The reduction in CO2 emission is demonstrated as a function of cross-sectional dimensions while considering certain structural performance requirements. The outcome of the more recently developed social spider algorithm is compared to the outcome of the well-established harmony search technique. The objective of optimization was to minimize CO2 emissions associated with the fabrication of CFST stub columns. The effects of varying the wall thickness as well as the concrete compressive strength on CO2 emissions are visualized by using two different optimization techniques.Öğe Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach(Elsevier Science Sa, 2024) Cakiroglu, Celal; Aydin, Yaren; Bekdas, Gebrail; Isikdag, Umit; Sadeghifam, Aidin Nobahar; Abualigah, LaithSince the cooling systems used in buildings in hot climates account for a significant portion of the energy consumption, it is very important for both economy and environment to accurately predict the cooling load and consider it in building designs. This study aimed to maximize energy efficiency by appropriately selecting the features of a building that affect its cooling load. To this end, data-driven, accurate, and accessible tools were developed that enable the prediction of the cooling load of a building by practitioners. The study involves simulating the energy consumption of a mid-rise, double-story terrace house in Malaysia using building information modeling (BIM) and estimating the cooling load using ensemble machine learning models and genetic programming. Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) models have been developed and made available as an online interactive graphical user interface on the Streamlit platform. Furthermore, the symbolic regression technique has been utilized to obtain a closed-form equation that predicts the cooling load. The dataset used for training the predictive models comprised 94,310 data points with 10 input variables and the cooling load as the output variable. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used to measure the predictive model performances. The results of the machine learning models indicated successful prediction, with the CatBoost model achieving the highest score (R2 = 0.9990) among the four ensemble models and the predictive equation. The SHAP analysis determined the aspect ratio of the building as the most impactful feature of the building.Öğe Explainable ensemble learning predictive model for thermal conductivity of cement-based foam(Elsevier Sci Ltd, 2024) Cakiroglu, Celal; Batool, Farnaz; Islam, Kamrul; Nehdi, Moncef L.Cement-based foam has emerged as a strong contender in sustainable construction owing to its superior thermal and sound insulation properties, fire resistance, and cost-effectiveness. To effectively use cement-based foam as a thermal insulation material, it is important to accurately predict its thermal conductivity. The current study aims at coining an accurate methodology for predicting the thermal conductivity of cement-based foam using state-ofthe-art machine learning techniques. A comprehensive experimental dataset of 504 data points was developed and used for training ensemble learning models including XGBoost, CatBoost, LightGBM and Random Forest. The independent variables of this dataset affecting the thermal conductivity are the cast density, percentage of pozzolan, porosity, percentage of moisture, and duration of hydration in days. Using the Isolation Forest algorithm proved effective in detecting and eliminating outliers in the dataset. All the ensemble learning techniques explored in this study achieved superior predictive accuracy with a coefficient of determination greater than 0.98 on the test dataset. The influence of the input features on the thermal conductivity was visualized using the SHapley Additive exPlanations (SHAP) approach and individual conditional expectation (ICE) plots. The cast density had the greatest effect on thermal conductivity. The explainable machine learning models demonstrated superior accuracy, efficiency, and reliability in estimating the thermal insulation of cement-based foam, opening the door for wider acceptance of this material in sustainable energy efficient construction.Öğe Explainable machine learning models for predicting the axial compression capacity of concrete filled steel tubular columns(Elsevier Sci Ltd, 2022) Cakiroglu, Celal; Islam, Kamrul; Bekdas, Gebrail; Isikdag, Umit; Mangalathu, SujithConcrete-filled steel tubular (CFST) columns have been popular in the construction industry due to enhanced mechanical properties such as higher strength and ductility, higher seismic resistance, and aesthetics. Extensive experimental, numerical and analytical studies have been conducted in the past few decades to assess the structural response of CFST columns under various loading conditions. However, there is still uncertainty in predicting the capacity of CFST columns, and most of the current codes are conservative. In this paper, data-driven machine learning (ML) models have been developed to predict the axial compression capacity of rectangular CFST columns. An extensive database of 719 experiments was collected from literature and is randomly used to train, test, and validate the ML models. Seven ML models, namely lasso regression, random forest, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Gradient Boosting (CatBoost), are evaluated to predict the compression capacity of CFST stub columns under axial load. The performance of the different ML models in predicting the compressive strength of CFST columns is compared by different code equations prevalent in different parts of the world. It is found that LightGBM and CatBoost models performed better with an accuracy of 97.9% and 98.3%, respectively, compared to the existing design codes in predicting the capacity of CFST columns. Feature importance analyses and SHapley Additive explanations (SHAP) explain the ML model performances and make the developed models interpretable. Resistance factor is determined using the best performing ML model for compressive strength prediction of CFST stub columns following AISC 360-16 code provision.Öğe Manta Ray Foraging and Jaya Hybrid Optimization of Concrete Filled Steel Tubular Stub Columns Based on CO2 Emission(Springer Science and Business Media Deutschland GmbH, 2023) Cakiroglu, Celal; Islam, Kamrul; Bekdaş, GebrailConcrete-filled steel tubular (CFST) columns exhibit favorable characteristics and have been studied extensively particularly through experiments. However, the CO2 emission in the production process of these structural members should be reduced to minimize the environmental impact. At the same time, the performance of these structures should be kept at a satisfactory level. This can be achieved using metaheuristic optimization algorithms. The most commonly used indicator of structural performance for CFST columns is the ultimate axial load carrying capacity (Nu). This quantity can be predicted using various equations available in design codes and the research literature. However, most of these equations are only applicable within certain parameter ranges. A recently developed set of equations from the CFST literature was applied for the prediction of Nu due to its improved ranges of applicability. Furthermore, novel metaheuristic algorithms called Manta Ray Foraging Optimization and, Jaya algorithm are applied to the cross-section optimization of rectangular CFST columns. The improvement of the structural dimensioning under Nu constraint was demonstrated. The objective of optimization was to minimize the CO2 emission associated with the fabrication of CFST stub columns. For different concrete classes and load capacities, the optimum cross-sectional dimensions have been obtained. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP(Mdpi, 2023) Bekdas, Gebrail; Cakiroglu, Celal; Kim, Sanghun; Geem, Zong WooThe optimal design of prestressed concrete cylindrical walls is greatly beneficial for economic and environmental impact. However, the lack of the available big enough datasets for the training of robust machine learning models is one of the factors that prevents wide adoption of machine learning techniques in structural design. The current study demonstrates the application of the well-established harmony search methodology to create a large database of optimal design configurations. The unit costs of concrete and steel used in the construction, the specific weight of the stored fluid, and the height of the cylindrical wall are the input variables whereas the optimum thicknesses of the wall with and without post-tensioning are the output variables. Based on this database, some of the most efficient ensemble learning techniques like the Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Gradient Boosting (CatBoost) and Random Forest algorithms have been trained. An R-2 score greater than 0.98 could be achieved by all of the ensemble learning models. Furthermore, the impacts of different input features on the predictions of different machine learning models have been analyzed using the SHapley Additive exPlanations (SHAP) methodology. The height of the cylindrical wall was found to have the greatest impact on the optimal wall thickness, followed by the specific weight of the stored fluid. Also, with the help of individual conditional expectation (ICE) plots the variations of predictive model outputs with respect to each input feature have been visualized. By using the genetic programming methodology, predictive equations have been obtained for the optimal wall thickness.Öğe Parametric Study of Dispersed Laminated Composite Plates(Springer Nature, 2021) Cakiroglu, Celal; Bekdaş, GebrailLaminated composite plates with one core layer and two surface layers are simulated in an extensive parametric analysis. The main objective of these simulations is to analyze the load-carrying capacity and how it varies with regard to changing stacking sequences. In total, 7290 unique combinations of the ply thicknesses and fiber orientation angles are simulated using the finite element analysis software Abaqus. The stacking sequences are ranked according to their corresponding buckling loads, and the best performing ones are subjected to a closer analysis. The visualization of the results showed that for any given stack of fiber orientation angles, and the stacking sequence of the ply thicknesses can have a significant impact on the structural performance. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Öğe The effect of slenderness on the lateral-torsional buckling and ultimate shear capacity of plate girders(2020) Cakiroglu, Celal; Islam, Kamrul; Bekdaş, GebrailLateral torsional buckling and shear buckling are two of the most significant structural responses that should be considered during the design process of plate girders.Particularly the importance of lateral torsional buckling was once again witnessedduring the reconstruction process of a bridge in Edmonton, Alberta, Canada when theplate girders failed due to insufficient bracing. This current study aims to acquire abetter understanding of the effect of geometric parameters such as the web slenderness, flange slenderness and span-to-depth ratio on the critical buckling moment andultimate shear strength of plate girders. To achieve this goal the critical buckling moment and ultimate shear strength of a plate girder were parametrically studied for alarge number of geometries using a load case from an experimental study. The resultsof this parametric study clarified the effects of web slenderness, flange slendernessand span-to-depth ratio on the structural performance of a plate girder. The visualization of the results was used to identify the ranges of these geometric parameterswhere the structural performance is most sensitive to changing them.
