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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 Data-driven ensemble learning approach for optimal design of cantilever soldier pile retaining walls(Elsevier, 2023) Çakıroğlu, Celal; Islam, Kamrul; Bektaş, Gebrail; Nehdi, Moncef L.Cantilever soldier pile retaining walls are used to ensure the stability of excavations. This paper deploys ensemble machine learning algorithms towards achieving optimum design of these structures. A large dataset was developed consisting of 40,569 combinations of pile geometry, external loading, soil properties, and concrete unit cost, with two different values of soil reaction coefficient. Optimum pile diameter that minimizes the total cost of the retaining wall was computed considering the structural load-carrying capacity as the optimization constraint. The dataset was split into training and testing sets at 70% to 30% ratio. The predictive accuracy of the ensemble machine learning models was appraised on the testing dataset using various statistical metrics. Model performance was also evaluated for its ability in predicting the optimum pile diameter. The developed models demonstrated excellent predictive accuracy. Furthermore, the effect of different input variables on the model predictions was explained using the SHapely Additive exPlanations (SHAP) approach. Through the SHAP algorithm, the pile length was identified as the design variable having the most significant effect on the optimum pile diameter. The study demonstrates ensemble learning techniques as a viable alternative to the traditional techniques in the optimum design of cantilever soldier pile retaining walls.Öğe Effectiveness of wood waste sawdust to produce medium- to low-strength concrete materials(Elsevier, 2021) Batool, Farnaz; Islam, Kamrul; Çakıroğlu, Celal; Shahriar, AnjumanThis paper investigates the use of sawdust as fine aggregate and its influence on the properties of hardened concrete, and examines the correlation between sawdust content and hydration days. In this study, untreated wood sawdust from a wood factory in Bangladesh is added to concrete mixtures. Concrete mixtures prepared by replacing fine aggregates with sawdust in the ratio of 10%-60% are evaluated for compressive, tensile, and flexural strength along with sulphate resistance for four different hydration periods. In addition, the microstructure of sawdust concrete is studied using scanning electron microscopic images. The micrographs show a wider formation of cracks, openings, and interface gaps in the cement matrix with the addition of sawdust. However, after sulphate immersion, the gaps and cracks are found to contract due to the ettringite filler effect. The addition of sawdust is found to reduce the workability and to have an adverse effect with increasing replacement levels. Similarly, reduced density of the hardened concrete is observed in the case of the sawdust concrete mixtures. Experimental results and cost comparison reveal that ten percent sawdust substitution is found to be optimal and low-cost replacement to natural fine aggregates with respect to the hardened properties, as it yields better performance in comparison with the other replacement ratios. Also, sulphate immersion for a period of 28 days is found to improve the compressive strength, even for mixtures with higher sawdust content. Moreover, a model is developed to predict the compressive and tensile strength of sawdust concrete using regression analysis.Öğe Explainable ensemble learning data-driven modeling of mechanical properties of fiber-reinforced rubberized recycled aggregate concrete(Elsevier, 2023) Çakıroğlu, Celal; Shahjalal, Md.; Islam, Kamrul; Mahmood, S.M. Faisal; Billah, A.H.M. MuntasirColossal amounts of construction and demolition waste (C&D) and waste tires have become a considerable global environmental concern. To alleviate this issue, it is proposed to use crumb rubber (CR) derived from waste tires and recycled coarse aggregate (RCA) from C&D as a replacement for natural aggregates in new construction materials. However, the wide variability in the mechanical properties of recycled concrete and the lack of reliable predictive tools in the literature make the wide-scale adoption of these new materials a challenging task. Robust methodologies for predicting the mechanical properties of these materials are needed to advance them as viable alternatives to natural aggregates. Hence, this study compiled a comprehensive experimental database comprising 451, 151, and 102 samples from the literature, including compressive, tensile, and flexural strength values of fiber-reinforced rubberized recycled aggregate concrete (FRRAC). Based on these experimental results, seven data-driven machine learning models were developed. A total of 16 input variables were considered in developing these machine-learning models. It was demonstrated that the CatBoost model performed best for predicting the compressive and tensile strengths, whereas for flexural strength, Random Forest models provided better performance. According to SHapley Additive exPlanations (SHAP) values, the age of concrete, fineness modulus of the natural fine aggregate and the replacement percentage of the RCA were the most impactful input features in the prediction of the compressive, tensile, and flexural strength, respectively. Moreover, it was found that the usage of fiber reinforcement increased the impact of the w/c ratio. Based on the results, it is suggested to limit the replacement level of RCA and CR to 30% and 15%, respectively. Finally, this study highlights the importance of data-driven models in optimizing the mechanical properties of FRRAC, offering a useful tool for industry-scale developments.Öğe Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete(Elsevier, 2024) Çakıroğlu, Celal; Tusher, Tanvir Hassan; Shahjalal, Md.; Islam, Kamrul; Billah, A. H. M. Muntasir; Nehdi, Moncef L.Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. Prior investigations have solely concentrated on bond strength, resulting in a limited comprehension of the bond failure pattern. Considering the increasing significance of sustainable construction methods, it is crucial to examine both the failure pattern and bond strength to expand the versatility of RCA in various reinforced concrete structures. Accordingly, XGBoost, CatBoost, Random Forest, and LightGBM were trained for this purpose. Model performance was appraised using various statistical metrics, while failure classification performance was assessed using accuracy, recall, and precision indicators. Model performance was ranked using Copeland’s algorithm. Feature importance was quantified using SHAP. Coefficient of determination of 0.91 was achieved by XGBoost in predicting bond strength, outperforming other nine analytical models in literature. Failure mode was predicted with accuracy of 94% by CatBoost, XGBoost, and LightGBM. Embedment length and compressive strength features had greatest influence on bond strength and failure mode, respectively. User-friendly graphical interface was developed to harvest ML models in real-world engineering practice. Online free access accurately assigns to any given combination of input features corresponding accurate rebar bond strength and failure mode.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 model for predicting punching shear strength of FRC flat slabs(2024) Liu, Tongxu; Çakıroğlu, Celal; Islam, Kamrul; Wang, Zhen; Nehdi, Moncef L.Reinforced concrete slabs are vulnerable to punching shear failure at the slab-column joint, which can initiate catastrophic progressive collapse. The addition of steel fibers in the concrete matrix has emerged as an effective strategy to mitigate such progressive failure. However, the effects of the diverse mixture proportions of the concrete matrix with different types and dosages of fibers have made the accurate prediction of the punching shear strength (PSS) of the fiber-reinforced concrete (FRC) flat slabs a complex task, where the existing me chanical models have several limitations. Therefore, this study proposes an explainable XGBoost model for predicting PSS of flat slabs made with different types of FRC based on a newly established comprehensive database of 251 flat slabs including normal strength FRC slabs, high-performance FRC slabs, and ultra-highperformance FRC slabs. A customized procedure was proposed to establish the XGBoost model considering data preparation, feature selection, hyperparameter tuning and model validation. The performance of the XGBoost model was then compared with that of existing mechanical models. Finally, sensitivity analysis and SHapley Additive exPlanations (SHAP) analysis were applied to identify the most influential parameters on the prediction of PSS. Results show that the proposed feature selection method is effective in identifying six influ ential parameters from the eleven parameters related to the PSS of FRC flat slabs. The developed XGBoost model yielded highest prediction accuracy and lowest variation, which outperformed the other mechanical models. Sensitivity analysis also indicated similar trends of parameters in both the XGBoost model and the mechanical models. The PSS of FRC flat slabs can be improved by increasing the concrete compressive strength, reinforce ment ratio, and fiber volume, and by decreasing the column width-to-depth ratio, water-to-binder ratio, and aggregate size ratio. The proposed XGBoost model could enhance the understanding of PSS of FRC flat slabs and guide future pertinent design code provisions.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 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 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 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 Optimum design of cylindrical walls using ensemble learning methods(MDPI-Multidisciplinary Digital Publishing Institute, 2022) Bekdaş, Gebrail; Çakıroğlu, Celal; Islam, Kamrul; Kim, Sanghun; Geem, Zong WooThe optimum cost of the structure design is one of the major goals of structural engineers. The availability of large datasets with preoptimized structural configurations can facilitate the process of optimum design significantly. The current study uses a dataset of 7744 optimum design configurations for a cylindrical water tank. Each of them was obtained by using the harmony search algorithm. The database used contains unique combinations of height, radius, total cost, material unit cost, and corresponding wall thickness that minimize the total cost. It was used to create ensemble learning models such as Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Gradient Boosting (CatBoost). Generated machine learning models were able to predict the optimum wall thickness corresponding to new data with high accuracy. Using SHapely Additive exPlanations (SHAP), the height of a cylindrical wall was found to have the greatest impact on the optimum wall thickness followed by radius and the ratio of concrete unit cost to steel unit cost.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.
