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Öğe A review on the mechanical properties of natural fiber-reinforced composites(Springer Science and Business Media Deutschland GmbH, 2021) Çakıroğlu, Celal; Bekdaş, GebrailNatural fibers have drawn attention from the researchers and engineers in recent years due to their mechanical properties comparable to the conventional synthetic fibers and due to their low cost, eco-friendliness, and bio-degradability. Therefore, natural fibers such as kenaf or flux are being considered as a viable replacement for glass, aramid, or carbon. Extensive experimental studies are being carried out in order to determine the mechanical behavior of different natural fiber types such as the elastic modulus tensile strength, flexural strength, and the Poisson’s ratio. This paper is a review of the various experimental studies in this field and it summarizes the findings of the researchers about the elastic properties of the major types of natural fiber-reinforced composites. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Öğe Buckling analysis and stacking sequence optimization of symmetric laminated composite plates(Springer Science and Business Media Deutschland GmbH, 2021) Çakıroğlu, Celal; Bekdaş, GebrailSymmetric laminated composite plates are frequently used in structural design due to their high strength to weight ratio. The stacking sequence of these laminates is known to have a major impact on the performance of these structural members. As a measure of performance often the buckling load of a laminated composite plate is used. In this study the buckling load of a laminated composite plate is obtained using analytical techniques. In order to optimize the structural performance a newly developed and very efficient meta-heuristic technique called the Jaya algorithm has been utilized. The fiber orientation angles of the plies as well as the ply thicknesses are regarded as continuous valued design variables and allowed to vary randomly while keeping the symmetry of the configuration which resulted in dispersed symmetric plates. CFRP is chosen as the plate material due to being the most frequently used composite material in the industry. The material properties and plate aspect ratio are treated as constant values. It was shown that allowing the fiber orientation angles to take values other than the commonly used 0°, ± 45°, 90° combined with proper thickness sequence can greatly enhance the structural performance. © 2021, Springer Nature Switzerland AG.Öğe Buckling analysis of natural fiber reinforced composites(2021) Çakıroğlu, Celal; Bekdaş, GebrailIn the recent years natural fiber reinforced composites are increasingly receiving attention from the researchers and engineers due to their mechanical properties comparable to the conventional synthetic fibers and due to their ease of preparation, low cost and density, eco-friendliness and bio-degradability. Natural fibers such as kenaf or flux are being considered as a viable replacement for glass, aramid or carbon. Extensive experimental studies have been carried out to determine the mechanical behavior of different natural fiber types such as the elastic modulus, tensile strength, flexural strength and the Poisson’s ratio. This paper presents a review of the various experimental studies in the field of fiber reinforced composites while summarizing the research outcome about the elastic properties of the major types of natural fiber reinforced composites. Furthermore, the performance of a kenaf reinforced composite plate is demonstrated using finite element analysis and results are compared to a glass fiber reinforced laminated composite plate.Öğe CO2 emission and cost optimization of concrete-filled steel tubular (CFST) columns using metaheuristic algorithms(MDPI, 2021) Çakıroğlu, Celal; Billah, Muntasir; İslam, Kamrul; Bekdaş, GebrailConcrete-filled steel tubular columns have garnered wide interest among researchers due to their favorable structural characteristics. To attain the best possible performance from concrete-filled steel tubular columns while reducing the cost, the use of optimization algorithms is indispensable. In this regard, metaheuristic algorithms are finding increasing application in structural engineering due to their high efficiency. Various equations that predict the ultimate axial load-carrying capacity (N-u) of concrete-filled steel tubular columns are available in design codes as well as in the research literature. However, most of these equations are only applicable within certain parameter ranges. To overcome this limitation, the present study adopts a recently developed set of equations for the prediction of Nu that have broader ranges of applicability. Furthermore, a newly developed metaheuristic algorithm, called the social spider algorithm, is introduced and applied in optimizing the cross-section of circular concrete-filled steel tubular columns. The improvement of the structural dimensioning under the Nu constraint is demonstrated. The objective underlying the optimization presented here is to minimize the CO2 emission and cost associated with the fabrication of concrete-filled steel tubular stub columns. In this context, the relationships between the cross-sectional dimensioning of circular concrete-filled steel tubular columns and the associated CO2 emissions and cost are characterized and visualized.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 Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach ((2024) Çakıroğlu, Celal; Aydın, Yaren; Bekdaş, Gebrail; Işıkdağ, Ümit; 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 and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete(2024) Çakıroğlu, Celal; Bekdaş, Gebrail; Geem, Zong Woo; Aydın, Yarenfirst_pagesettingsOrder Article Reprints Open AccessArticle Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete by Yaren Aydın 1ORCID,Celal Cakiroglu 2ORCID,Gebrail Bekdaş 1,*ORCID andZong Woo Geem 3,*ORCID 1 Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey 2 Department of Civil Engineering, Turkish-German University, 34820 Istanbul, Turkey 3 Department of Smart City, Gachon University, Seongnam 13120, Republic of Korea * Authors to whom correspondence should be addressed. Biomimetics 2024, 9(9), 544; https://doi.org/10.3390/biomimetics9090544 Submission received: 27 June 2024 / Revised: 23 August 2024 / Accepted: 5 September 2024 / Published: 9 September 2024 (This article belongs to the Special Issue Bionic Design & Lightweight Engineering) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model’s performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R2 score of 0.971 on the test set. On the other hand, the average R2 score of the MLP model was 0.909. The results of the SHAP analysis showed that the age of concrete and the amounts of silica fume, fiber, superplasticizer, cement, aggregate, and water have the greatest impact on the model predictions.Öğe Explainable ensemble learning models for the rheological properties of self-compacting concrete(MDPI-Multidisciplinary Digital Publishing Institute, 2022) Çakıroğlu, Celal; Bekdaş, Gebrail; Kim, Sanghun; Geem, Zong WooSelf-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable's effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity.Öğe Harmony search optimisation of dispersed laminated composite plates(Mdpi, 2020) Çakıroğlu, Celal; Bekdaş, Gebrail; Geem, Zong WooOne of the major goals in the process of designing structural components is to achieve the highest possible buckling load of the structural component while keeping the cost and weight at a minimum. This paper illustrates the application of the harmony search algorithm to the buckling load maximisation of dispersed laminated composite plates with rectangular geometry. The ply thicknesses and fiber orientation angles of the plies were chosen as the design variables. Besides the commonly used carbon fiber reinforced composites, boron/epoxy and glass/epoxy composite plates were also optimised using the harmony search algorithm. Furthermore, the optimisation algorithm was applied to plates with three different aspect ratios (ratio of the longer side length to the shorter side length of the plate). The buckling loads of the plates with optimised dispersed stacking sequences were compared to the buckling loads of plates with the commonly applied 0 degrees, +/- 45 degrees, and 90 degrees fiber angle sequence and identical ply thicknesses. For all three aspect ratios and materials in this study, the dispersed stacking sequences performed better than the plates with regular stacking sequences.Öğe Interpretable machine learning algorithms to predict the axial capacity of frp-reinforced concrete columns(MDPI-Multidisciplinary Digital Publishing Institute, 2022) Çakıroğlu, Celal; Islam, Kamrul; Bekdaş, Gebrail; Kim, Sanghun; Geem, Zong WooFiber-reinforced polymer (FRP) rebars are increasingly being used as an alternative to steel rebars in reinforced concrete (RC) members due to their excellent corrosion resistance capability and enhanced mechanical properties. Extensive research works have been performed in the last two decades to develop predictive models, codes, and guidelines to estimate the axial load-carrying capacity of FRP-RC columns. This study utilizes the power of artificial intelligence and develops an alternative approach to predict the axial capacity of FRP-RC columns more accurately using data-driven machine learning (ML) algorithms. A database of 117 tests of axially loaded FRP-RC columns is collected from the literature. The geometric and material properties, column shape and slenderness ratio, reinforcement details, and FRP types are used as the input variables, while the load-carrying capacity is used as the output response to develop the ML models. Furthermore, the input-output relationship of the ML model is explained through feature importance analysis and the SHapely Additive exPlanations (SHAP) approach. Eight ML models, namely, Kernel Ridge Regression, Lasso Regression, Support Vector Machine, Gradient Boosting Machine, Adaptive Boosting, Random Forest, Categorical Gradient Boosting, and Extreme Gradient Boosting, are used in this study for capacity prediction, and their relative performances are compared to identify the best-performing ML model. Finally, predictive equations are proposed using the harmony search optimization and the model interpretations obtained through the SHAP algorithm.Öğe Introduction and Review on Active Structural Control(Springer Science and Business Media Deutschland GmbH, 2022) Ulusoy, Serdar; Nigdeli, Sinan Melih; Bekdaş, GebrailThe number of stories of structures has been increased to meet the needs of people or long-span bridges are built for easier transportation. However, various destructive dynamic loads such as earthquakes and strong winds have negative effects on the structure without control systems. Therefore, control systems are necessary for these constructions. Furthermore, it is not enough for these constructions to be just safe and reliable. Constructions should be less exposed to vibrations under the influence of earthquakes and strong winds. For this reason, an external power source (activator) that can handle the large horizontal loads in these structures and a system that controls this source are needed. This study summarizes different active control systems, control techniques and studies on active structural control. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.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 Metaheuristic algorithms in optimum design of reinforced concrete beam by investigating strength of concrete(2020) Ulusoy, Serdar; Kayabekir, Aylin Ece; Bekdaş, Gebrail; Niğdeli, Sinan MelihThe locations of structural members can be provided according to architectural pro-jects in the design of reinforced concrete (RC) structures. the design of dimensions isthe subject of civil engineering, and these designs are done according to the expe-rience of the designer by considering the regulation suggestions, but these dimen-sions and the required reinforcement plan may not be optimum. For that reason, the dimensions and detailed reinforcement design of RC structures can be found by using optimization methods. To reach optimum results, metaheuristic algorithms can be used. In this study, several metaheuristic algorithms such as harmony search, bat al-gorithm and teaching learning-based optimization are used in the design of several RC beams for cost minimization. the optimum results are presented for different strength of concrete. the results show that using high strength material for high flex-ural moment capacity has lower cost than low stretch concrete since doubly rein-forced design is not an optimum choice. the results prove that a definite metaheuris-tic algorithm cannot be proposed for the best optimum design of an engineering problem. According to the investigation of compressive strength of concrete, it can be said that a low strength material are optimum for low flexural moment, while a high strength material may be the optimum one by the increase of the flexural mo-ment as expected.Öğe Metaheuristic optimization of laminated composite plates with Cut-Outs(MDPI-Multidisciplinary Digital Publishing Institute, 2021) Çakıroğlu, Celal; Islam, Kamrul; Bekdaş, Gebrail; Kim, Sanghun; Geem, Zong WooThe stacking sequence optimization of laminated composite plates while maximizing the structural performance or minimizing the weight is a subject investigated extensively in the literature. Meanwhile, research on the optimization of laminates with cut-outs has been relatively limited. Cut-outs being an indispensable feature of structural components, this paper concentrates on the stacking sequence optimization of composite laminates in the presence of circular cut-outs. The buckling load of a laminate is used as a metric to quantify the structural performance. Here the laminates are modeled as carbon fiber-reinforced composites using the finite element analysis software, ABAQUS. For the optimization, the widely used harmony search algorithm is applied. In terms of design variables, ply thickness, and fiber orientation angles of the plies are used as continuously changing variables. In addition to the stacking sequence, another geometric variable to consider is the aspect ratio (ratio of the length of the longer sides to the length of the shorter sides of the plate) of the rectangular laminates. The optimization is carried out for three different aspect ratios. It is shown that, by using dispersed stacking sequences instead of the commonly used 0 & DEG;/& PLUSMN;45 & DEG;/& PLUSMN;90 & DEG; fiber angle stacks, significantly higher buckling loads can be achieved. Furthermore, changing the cut-out geometry is found to have a significant effect on the structural performance.Öğe Neural Network Predictive Models for Alkali-Activated Concrete Carbon Emission Using Metaheuristic Optimization Algorithms(2024) Aydın, Yaren; Çakıroğlu, Celal; Bekdaş, Gebrail; Işıkdağ, Ümit; Kim, Sanghun; Hong, Junhee; Geem, Zong WooDue to environmental impacts and the need for energy efficiency, the cement industry aims to make more durable and sustainable materials with less energy requirements without compromising mechanical properties based on UN Sustainable Development Goals 9 and 11. Carbon dioxide (CO2) emission into the atmosphere is mostly the result of human-induced activities and causes dangerous environmental impacts by increasing the average temperature of the earth. Since the production of ordinary Portland cement (PC) is a major contributor to CO2 emissions, this study proposes alkali-activated binders as an alternative to reduce the environmental impact of ordinary Portland cement production. The dataset required for the training processes of these algorithms was created using Mendeley as a data-gathering instrument. Some of the most efficient state-of-the-art meta-heuristic optimization algorithms were applied to obtain the optimal neural network architecture with the highest performance. These neural network models were applied in the prediction of carbon emissions. The accuracy of these models was measured using statistical measures such as the mean squared error (MSE) and coefficient of determination (R2). The results show that carbon emissions associated with the production of alkali-activated concrete can be predicted with high accuracy using state-of-the-art machine learning techniques. In this study, in which the binders produced by the alkali activation method were evaluated for their usability as a binder material to replace Portland cement, it is concluded that the most successful hyperparameter optimization algorithm for this study is the genetic algorithm (GA) with accurate mean squared error (MSE = 161.17) and coefficient of determination (R2 = 0.90) values in the datasets.Öğe Novel metaheuristic-based tuning of PID controllers for seismic structures and verification of robustness(Elsevier Ltd, 2021) Ulusoy, Serdar; Niğdeli, Sinan Melih; Bekdaş, GebrailIn the present study, an active structural control using metaheuristic tuned Proportional-Integral-Derivative (PID) type controllers is presented. The aim of the study is to propose a feasible active control application considering time delay and a feasible control force. In the optimum control methodology, near-fault directivity pulse was considered for ground motion. Three different metaheuristic algorithms are separately employed in the optimum tuning of PID parameters such as proportional gain, integral time and derivative time. The employed algorithms are Flower Pollination Algorithm, Teaching Learning Based Optimization and Jaya algorithm. The maximum control force limit is considered as a design constraint. The methodology contains the time delay consideration and a process to avoid the stability problem on the trial results during the optimization process. The method is explained in three stages as The Pre-Optimization Stage, The Dynamic Analysis Stage and The Optimization Stage. The optimum PID parameters of different algorithms are very different, but the performance of active control is similar since a similar control signal can be generated by different proportion of controller gains such as proportion, integral and derivative processes. As the conclusion of the study, the amount of control force must be chosen carefully since big control forces may resulted with stability problems if the control system has long delay. © 2020Öğe Optimal dimensioning of retaining walls using explainable ensemble learning algorithms(MDPI-Multidisciplinary Digital Publishing Institute, 2022) Bekdaş, Gebrail; Çakıroğlu, Celal; Kim, Sanghun; Geem, Zong WooThis paper develops predictive models for optimal dimensions that minimize the construction cost associated with reinforced concrete retaining walls. Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were applied to obtain the predictive models. Predictive models were trained using a comprehensive dataset, which was generated using the Harmony Search (HS) algorithm. Each data sample in this database consists of a unique combination of the soil density, friction angle, ultimate bearing pressure, surcharge, the unit cost of concrete, and six different dimensions that describe an optimal retaining wall geometry. The influence of these design features on the optimal dimensioning and their interdependence are explained and visualized using the SHapley Additive exPlanations (SHAP) algorithm. The prediction accuracy of the used ensemble learning methods is evaluated with different metrics of accuracy such as the coefficient of determination, root mean square error, and mean absolute error. Comparing predicted and actual optimal dimensions on a test set showed that an R-2 score of 0.99 could be achieved. In terms of computational speed, the LightGBM algorithm was found to be the fastest, with an average execution speed of 6.17 s for the training and testing of the model. On the other hand, the highest accuracy could be achieved by the CatBoost algorithm. The availability of open-source machine learning algorithms and high-quality datasets makes it possible for designers to supplement traditional design procedures with newly developed machine learning techniques. The novel methodology proposed in this paper aims at producing larger datasets, thereby increasing the applicability and accuracy of machine learning algorithms in relation to optimal dimensioning of structures.Öğe Optimisation of shear and lateral-torsional buckling of steel plate girders using meta-heuristic algorithms(Mdpi, 2020) Çakıroğlu, Celal; Bekdaş, Gebrail; Kim, Sanghun; Geem, Zong WooThe shear buckling of web plates and lateral-torsional buckling are among the major failure modes of plate girders. The importance of the lateral-torsional buckling capacity of plate girders was further evidenced when several plate girders of a bridge in Edmonton, Alberta, Canada failed in 2015, because insufficient bracing led to the lateral buckling of the plate girders. In this study, we focus on the optimisation of the cross-sections of plate girders using a well-known and extremely efficient meta-heuristic optimisation algorithm called the harmony search algorithm. The objective of this optimisation is to design the cross-sections of the plate girders with the minimum area that satisfies requirements, such as the lateral-torsional buckling load and ultimate shear stress. The base geometry, material properties, applied load and boundary conditions were taken from an experimental study and optimised. It was revealed that the same amount of load-carrying capacity demonstrated by this model can be achieved with a cross-sectional area 16% smaller than that of the original specimen. Furthermore, the slenderness of the web plate was found to have a decisive effect on the cost-efficiency of the plate girder design.Öğe Optimization and predictive modeling of reinforced concrete circular columns(MDPI-Multidisciplinary Digital Publishing Institute, 2022) Bekdaş, Gebrail; Çakıroğlu, Celal; Kim, Sanghun; Geem, Zong WooMetaheuristic optimization techniques are widely applied in the optimal design of structural members. This paper presents the application of the harmony search algorithm to the optimal dimensioning of reinforced concrete circular columns. For the objective of optimization, the total cost of steel and concrete associated with the construction process were selected. The selected variables of optimization include the diameter of the column, the total cross-sectional area of steel, the unit costs of steel and concrete used in the construction, the total length of the column, and applied axial force and the bending moment acting on the column. By using the minimum allowable dimensions as the constraints of optimization, 3125 different data samples were generated where each data sample is an optimal design configuration. Based on the generated dataset, the SHapley Additive exPlanations (SHAP) algorithm was applied in combination with ensemble learning predictive models to determine the impact of each design variable on the model predictions. The relationships between the design variables and the objective function were visualized using the design of experiments methodology. Applying state-of-the-art statistical accuracy measures such as the coefficient of determination, the predictive models were demonstrated to be highly accurate. The current study demonstrates a novel technique for generating large datasets for the development of data-driven machine learning models. This new methodology can enhance the availability of large datasets, thereby facilitating the application of high-performance machine learning predictive models for optimal structural design.Öğe Optimization of axial load carrying capacity of CFST stub columns(2022) Çakıroğlu, Celal; Bekdaş, GebrailConcrete filled steel tubular (CFST) columns are widely used due to their enhanced mechanical properties. The interaction between the concrete core and the steel casing increases structural stability and magnifies the compressive strength of concrete. Besides the structural performance, in alignment with the commitment of the concrete industry to reduce its environmental impact, lowering the carbon emissions caused by the production of concrete structures is gaining importance in recent years. The current paper gives an overview of the equations available in the literature that predict the axial load carrying capacity of rectangular CFST columns. A modified version of the Jaya metaheuristic algorithm is being proposed and the outcome of this algorithm is being presented. The algorithm is used in order to maximize the axial load-carrying capacity of a stub column. As an optimization constraint the CO2 emission associated with the production of the CFST column is being kept below a predefined level throughout the optimization process. The optimization process as well as the cross-sectional dimensions associated with the optimum solution are presented.
