Yazar "Katlav, Metin" seçeneğine göre listele
Listeleniyor 1 - 20 / 27
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe AI-driven sustainable design: Interpretable PSO-ML framework for modeling of splitting tensile strength in steel fiber-reinforced recycled aggregate concrete(Ernst & Sohn, 2026) Utu, Rumeysa; Katlav, Metin; Turk, KazimThe construction industry's intensive resource use and waste generation necessitate sustainable alternatives. Using recycled coarse aggregates in concrete offers a viable solution. Accurately predicting the splitting tensile strength (STS) of such concretes through data-driven methods reduces experimental needs, conserves materials, and enhances design efficiency and cost-effectiveness. With this aim, this research thoroughly investigates the feasibility of optimized machine learning (ML) algorithms through particle swarm optimization (PSO) for modeling the STS of eco-friendly steel fiber-reinforced recycled coarse aggregate concrete (SRCAC). Accordingly, based on experimental data in the literature, a comprehensive and reliable database consisting of 335 STS values with 11 input features was adopted. Additionally, SHapley Additive exPlanations (SHAP)-based feature importance and sensitivity analyses were implemented to make the black box behavior of PSO-ML models more transparent and to provide insight into the model's decision-making process. Given the results obtained, the PSO-ML models developed in this paper demonstrate high accuracy, stability, and generalizability in data-driven forecasting of STS of SRCAC, proving to be an effective tool for engineering applications. Specifically, the PSO-CatBoost and PSO-gradient boosting machine (GBM) models stand out with their high R2 values (0.931 and 0.894, respectively) and low root mean squared error (RMSE) values (0.58 and 0.73, respectively) on the testing set, demonstrating that these models can be used as reliable decision support systems, thanks to their prediction accuracy as well as their low variance and balanced distribution. In addition, based on SHAP-based explainability analyses, features such as the water/binder ratio (W/B), fiber factor (F), fine aggregate content (FA), and fiber length (l f) were determined as the most decisive parameters on STS, whereas the relative effect of variables like fiber volume fraction (V f) and fiber aspect ratio (l f /d f) remained limited. All in all, the PSO-ML framework introduced makes it possible to forecast the STS value of SRCAC with high accuracy without the need for experimental testing, providing an important digital tool for sustainable and economical concrete design. These models not only reduce the use of natural resources by determining the optimal design parameters for concrete mixes containing recycled materials but also support environmental sustainability by promoting the reuse of construction waste and reducing the carbon footprint. Thus, data-driven decision-making processes enable the development of more effective, transparent, and eco-friendly design strategies in civil engineering applications.Öğe AI-guided design framework for bond behavior of steel-concrete in steel reinforced concrete composites: From dataset cleaning to feature engineering(Elsevier, 2025) Katlav, Metin; Tabar, Mehmet Emin; Turk, KazimThis research focuses on establishing an artificial intelligence (AI)-guided design approach for predicting bond behavior of profiled steel-concrete in steel-reinforced concrete (SRC) composites. For that, an extensive literature survey was undertaken, and datasets for three main characteristic bond stresses-bond stress at initial slip (zs), ultimate bond stress (zu), and residual bond stress (zr) -were gathered. In total, it was gathered data points 150 for zs, 251 for zu, and 215 for zr. In addition, the isolation forest algorithm was used to detect and clean the anomalous data in the dataset, resulting in exhaustive and trustworthy data for training the models. As AI models, four popular machine learning algorithms like RF, XGBoost, LightGBM, and CatBoost are adopted. To improve the prediction performance of the models, three cases are established by Shapley additive explanations (SHAP)-based feature engineering. Additionally, SHAP and feature importance analyses were used to examine the impact of each feature on the bond behavior in SRC composites to ensure the explainability of the model. Meanwhile, to enhance the applicability of the study in real-world applications, a graphical user interface (GUI) was designed. According to the results, the CatBoost model proved its superior predictive ability specifically for zs and zr output values; in the test phase, the RMSE values were 0.07 and 0.05, R2 values were 0.904 and 0.934, MAPE were 10.02% and 8.33%, and MAE values were 0.04 and 0.03, respectively. On the other hand, the XGBoost model had the best predictive efficiency in the test phase for the zu output value with RMSE = 0.06, R2 = 0.833, MAPE = 8.32 % and MAE = 0.04. Lastly, based on SHAP and feature importance assessments, the most impactful features on bond behavior were identified as follows: the ratio of side cover to steel section height (cv/ hs), the compressive strength of concrete (fcu), and the ratio of bonded length to steel section height (lb/hs), stirrup ratio (psv), and the yield strength of profiled steel (fy). This knowledge can guide engineers in paying focus to specific features in their design and evaluation processes, resulting in more reliable and optimized outcomes.Öğe An intelligent framework for compressive strength prediction of eco-friendly SFR-RCAC: Base and stacked ensemble models combined with experimental verification(Elsevier Sci Ltd, 2025) Katlav, Metin; Tabar, Mehmet Emin; Turk, KazimThis paper adopts an approach based on base and stacked ensemble models to correctly model the compressive strength (CS), which is a key parameter to provide structural integrity and reliability of steel fiber-reinforced recycled coarse aggregate concrete (SFR-RCAC). To this end, a reliable framework is adopted that includes cleaned 440 instances with 11 input features. Additionally, the impact of the input features on the model is investigated in detail via SHapley additive explanation (SHAP) and partial dependence plots (PDPs) analyses. To facilitate practical implementation, a graphical user interface (GUI) is designed to make the estimation process user-friendly and its reliability is verified by additional experimental tests. Based on the results, all the developed models are capable of predicting the CS of SFR-RCAC with extraordinary accuracy and reliability: the 6 base ensemble models achieved an average R2 = 0.936 and RMSE = 3.55 during the testing phase, while the 35 stacked models recorded R2 = 0.942 and RMSE = 3.38, respectively. Notably, the stacked ensemble model (SM26) with the combination of Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Extra Trees Regressor (ETR) showed the best prediction performance in the test phase with the highest R2 (0.948) and the lowest RMSE (3.20) as well as the highest total score (287). Additionally, the error rate between the experimental values and the GUI predictions for the 10 designed mixes remains below +/- 8 %, verifying that the proposed GUI has high accuracy and robust generalization capability. Moreover, based on SHAP and PDP analyses, it is recommended for practical engineering applications to optimize the CS of SFR-RCAC by limiting the recycled coarse aggregate substitution ratio (Rr) to approximately 0.40, maintaining the steel fiber volume fraction (Vf) around 1.0 %, keeping the fiber factor (F) within the range of 0.6-0.8, and adjusting the water-tobinder (W/B) ratio between 0.30 and 0.40. To conclude, this research reveals the outstanding performance of the proposed models and GUI for predicting the CS value of SFR-RCAC and provides a significant contribution to the existing literature in this field. Thus, by promoting the efficient use of recycled coarse aggregates, it reduces the consumption of natural resources and allows the recycling of environmentally hazardous waste in the construction industry.Öğe Çelik lifli KYB ile üretilen V-şekilli betonarme katlanmış plak elemanların kalınlığı üzerinde farklı lif kombinasyonunun etkisi(İnönü Üniversitesi, 2022) Katlav, MetinKatlanmış plaklar, doğal rijitlikleri ve yüksek yük taşıma kapasitelerinin yanı sıra ekonomik avantajları ve estetik görünümleri nedeniyle bazı yapılarda (endüstriyel yapılar, depo, yüzme havuzları vb.) yaygın olarak kullanılmaktadır. Bu tür betonarme taşıyıcı elemanların uzun açıklıklı yapıların çatı taşıyıcı sisteminde kullanılması yapının hem daha hafif hem de daha ekonomik olmasına sağlayabilir. Bununla birlikte, betonarme katlanmış plakların kalınlığı, eğilme performansı açısından önemli bir parametredir. Kalınlığının eğilme performansı üzerindeki etkisi ile ilgili literatürde herhangi bir deneysel çalışma olmamasına rağmen, katlanmış plakların sayısal analizi üzerine yeterince araştırma bulunmaktadır. Bu çalışmada, çelik lif takviyeli kendiliğinden yerleşen betondan (KYB) üretilmiş V-şekilli betonarme katlanmış plakların kalınlığı üzerinde lif kombinasyonunun etkisi araştırılmıştır. Bu amaçla, kontrol, tek lifli ve karma lifli KYB karışımları, deneme-yanılma yoluyla elde edilmiş olup basınç, yarmada ve eğilmede çekme dayanımı testleri 3, 28 ve 90 günlük numuneler üzerinde belirlenmiştir. Daha sonra, KYB'den üretilen üç farklı plak kalınlığına (60, 70 ve 80 mm) sahip toplam on sekiz adet lifsiz, tek ve karma çelik lif takviyeli V-şekilli betonarme katlanmış plak numunesi hazırlanmıştır. 90 günlük V-şekilli betonarme katlanmış plaklar dört noktalı eğilme yüklemesine maruz bırakılarak, yük taşıma kapasitesi, yük-sehim davranışı, tokluk ve süneklik değerlerinin yanı sıra çatlak dağılımları bulunmuştur. Sonuç olarak, V-şekilli betonarme katlanmış plakların üretiminde özellikle karma çelik lif takviyeli KYB kullanımının plak kalınlığının azaltılması ve eğilme performansının iyileştirilmesi açısından önemli avantajlar sağladığı tespit edilmiştir. Ayrıca, çelik lif takviyeli KYB'den üretilmiş V-şekilli betonarme katlanmış plakların kalınlığının tahmini için regresyon analizi ile elde edilen ampirik bir formül önerilmiş ve bu formülün R2=0.97 ile yüksek doğrulukta bir tahmine sahip olduğu görülmüştür. Bununla beraber ACI 544 esas alınarak bazı varsayımlar ile çelik lif takviyeli KYB'den üretilmiş V-şekilli betonarme katlanmış plakların nominal moment taşıma kapasitesini tahmin etmek için bir tasarım yöntemi de önerilmiştir.Öğe Comprehensive experimental research on SCC with low hybrid fiber content: From workability to mechanical properties(Ernst & Sohn, 2026) Ari, Abdulkerim; Katlav, Metin; Turk, KazimHybrid fiber-reinforced self-compacting concrete (SCC) is gaining widespread popularity in construction engineering applications owing to its superior hardened concrete properties, while it isn't valid for workability, especially when it includes a high volume of fibers. While the incorporation of fibers can enhance the performance of SCC, it often challenges key fresh properties such as flowability and passing ability, thereby necessitating mix adjustments that result in additional costs. Furthermore, the high cost of fibers requires careful determination of the optimal fiber type combination and content. In this context, the aim and objective of this work is to comprehensively investigate the effects of different types (steel and synthetic) and combination (single, binary and ternary) of fibers on the workability (slump-flow, T500, J-ring and V-funnel) and mechanical (compressive strength, splitting tensile strength, elastic modulus, shear strength, and flexural tensile strength) properties of SCC mixes with low hybrid fiber content (total by volume 0.75%). After defining the workability properties of all mixes, mechanical property tests were performed on samples with curing periods of 7-, 28-, and 56-days. According to the results obtained, all fiber-reinforced SCC mixes exhibit high performance in terms of both workability and mechanical properties. In particular, binary hybrid fiber systems containing long hooked-end steel fibers and short straight steel fibers provided the best overall performance, yielding more effective outcomes compared to other mixes. In conclusion, this work has demonstrated that SCC mixes with low hybrid fiber content can successfully meet a wide range of engineering requirements, from workability to mechanical performance. This also implies that these mixes offer significant advantages in terms of both cost-effectiveness and ease of application due to workability superior of SCC mixes with low hybrid fiber content. These outcomes emphasize that SCC mixes with low hybrid fiber content can be safely used in structural concrete members subjected to complex loads such as punching, shear, and bending, and may offer more economical and sustainable alternatives to high-fiber content systems.Öğe Data-driven moment-carrying capacity prediction of hybrid beams consisting of UHPC-NSC using machine learning-based models(Elsevier Science Inc, 2024) Katlav, Metin; Ergen, FarukThis paper presents, for the first time in the literature, a study on the development of data-driven machine learning (ML) models to predict the moment-carrying capacity of ultra-high performance concrete (UHPC)normal strength concrete (NSC) hybrid beams. A database of 56 specimens of rectangular-section UHPC-NSC hybrid beams subjected to flexural loading is adopted to train the models. In this context, ten ML algorithms that are most preferred in structural engineering applications are selected to develop prediction-based models: linear regression (LR), lasso regression (LASSO), ridge regression (RR), support vector regression (SVR), multilayer perception (MLP), random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), K-nearest neighbors regression (KNN), and adaptive boosting regression (AdaBoost). Moreover, the Shapley additive explanation (SHAP) method is used to assess the impact of the input features on the prediction results. Lastly, user-friendly a graphical user interface (GUI) has been developed to ensure the interpretability of the prediction models and to overcome the black box problem of ML methods. The GUI, which is designed based on the model with the most effective prediction ability obtained from this work, allows design engineers to analyze their own data and customize the parameters of the model for the prediction of the moment-carrying capacity of UHPC-NSC hybrid beams. The results indicated that ML models can be an effective tool for predicting the moment-carrying capacity of UHPC-NSC hybrid beams. In this regard, notably, the XGBoost model exhibited superior performance in terms of prediction accuracy and generalization ability (R2 = 0.996 and 0.945 in the training and test datasets, respectively). On the other hand, according to the SHAP analysis results, the three most important input parameters influencing the moment-carrying capacity of UHPC-NSC hybrid beams are the effective depth (d), UHPC thickness at the bottom of the beam (UHPCbottom layer) and compressive strength of UHPC (fc,UHPC), respectively. Moreover, it has been found that the placement of the UHPC layer at the bottom of the beam rather than at the upper part of the beam is more effective in enhancing the moment-carrying capacity of UHPC-NSC hybrid beams.Öğe Effect of rebar arrangements on the structural behavior of RC folded plates manufactured from hybrid steel fiber-reinforced SCC(Elsevier, 2024) Turk, Kazim; Katlav, Metin; Turgut, PakiIn this paper, for the first time in the literature, the effect of different rebar arrangements on the structural behavior of reinforced concrete (RC) folded plates was investigated. For this purpose, RC folded plate specimens having various rebar arrangements fabricated from high-strength hybrid steel fiber-reinforced self-compacting concrete (HSFR-SCC) were tested by subjecting them to four-point bending loading. Then, the structural behavior properties of RC folded plates, such as crack patterns, failure mode, load-midspan displacement relationship, flexural stiffness, ductility, load-strain behavior, and moment-curvature response, were compared and thoroughly assessed. According to the experimental results, different rebar arrangements except for the detailing of transverse rebar induced the RC folded plates to exhibit a combined plate/beam-slab movement behavior, which resulted in higher load-carrying capacity. Notably, it can be said that the detailing of 90-degree hooked steel was generally more effective on the structural behavior of RC folded plates. In addition, the use of hybrid steel fiber instead of transverse rebar provided resistance against shear stresses at the joints and prevented the plates from separating. This application, in constructing the RC folded plates, can reduce labor costs, increase costeffectiveness, and shorten erection time. Therefore, it is obvious that the use of hybrid steel fiber as an alternative to the detailing of transverse rebar for the construction of RC folded plates will ensure some advantages. In conclusion, it is thought that the findings of the study can provide important guidance on the rebar arrangement of RC folded plates in structural engineering applications for structural engineers and designers.Öğe Electrical conductivity and heating performance of hybrid steel fiber-reinforced SCC: The role of high-volume fiber and micro fiber length(Elsevier, 2023) Turk, Kazim; Cicek, Nazli; Katlav, Metin; Donmez, Izzeddin; Turgut, PakiRecently, it has become very popular to develop electrically conductive concrete composites for active deicing and snow-melting of transportation infrastructure. These composites should have stable electrical conductivity and a uniform heating performance, as well as high mechanical and durability properties, for a sustainable solution. In this context, the main motivation of this study is to develop an electrically conductive hybrid steel fiber reinforced self-compacting concrete (HSFR-SCC) composite for ice and snow removal applications. The electrical conductivity and heating performance of self-compacting concrete (SCC) mixtures having different fiber volumes (1.00, 1.25, and 1.50%) and the combination of macro steel fiber with micro steel fibers having lengths of 13 and 6 mm as single and hybrid were experimentally investigated for the first time. For this purpose, a total of ten SCC mixtures were designed, one of which was non-fiber Control, the others had steel fiber volumes of 1.00, 1.25, and 1.50% and different fiber combinations. Workability (slump-flow, T500 and J-ring) tests on HSFR-SCC mixtures were performed with reference to EFNARC. Then, mechanical (compressive and flexural strengths), electrical resistivity, and heating performance tests of 90-day HSFR-SCC samples were carried out. Before the electrical resistivity and heating performance tests, HSFR-SCC samples were kept in an oven at 105 & PLUSMN; 5 degrees C for 24 h to measure their most critical state (dry) performance. Moreover, using multiple linear regression analysis, empirical equations and contour plots were developed to predict the electrical resistivity and temperature increase values of HSFR-SCC samples depending on fiber volumes and combinations. Considering the experimental results, electrically conductive HSFR-SCC mixtures with satisfactory workability and high strength were obtained. The addition of various volumes and combinations of steel fiber to SCC significantly improved the electrical conductivity and heating performance of the concrete, while the mixtures with hybrid fiber were the best for all fiber volumes. As for the different micro fibers added to the HSFR-SCC mixtures, the 13 mm length micro steel fiber was much more effective in improving the electrical resistivity and heating performance of the samples compared to the 6 mm length micro steel fiber. Also, it was found that the fiber-reinforced index of electrically conductive HSFR-SCC samples could be 0.87 for effective and efficient electrical conductivity.Öğe Estimation of the shear strength of UHPC beams via interpretable deep learning models: Comparison of different optimization techniques(Elsevier, 2024) Ergen, Faruk; Katlav, MetinIn this article, optimized deep learning (DL) models with different algorithms are adopted to estimate the shear strength of rectangular ultra-high performance concrete beams (UHPC-Bs) in order to overcome the challenges in traditional mechanics-based approaches. Long short-term memory (LSTM) and gated recurrent unit (GRU) are chosen as the DL models, whereas the recent popular optimization algorithms are phasor particle swarm optimization (PPSO), dwarf mongoose optimization (DMO), mountain gazelle optimizer (MGO), and atom search optimization (ASO). A thorough and reliable dataset of 244 UHPC-Bs test results with ten input features has been used to construct the hybrid DL models. The performance of the optimized hybrid LSTM and GRU models with different algorithms is extensively assessed and compared based on various statistical metrics, error, and score analyses. Then, the model with the best estimation performance is determined and compared with the mechanics-based formulas in the current international design codes. Additionally, Shapley additive explanations (SHAP) analysis is used to assist in the interpretability of DL models and to reveal the effects of input features that contribute to the model's estimation. According to the results of the present work, all DL models successfully estimate the shear strength of UHPC-Bs. Among these models, the MGO-LSTM model stands out compared to the other models in terms of several performance measures for both the training and testing phases, like a higher R-2 value, lower RMSE, MAPE, and MAE values, as well as a smaller error ratio and a higher final score. The performance of the algorithms applied to optimize the hyper-parameters of the LSTM and GRU models can be ranked as follows: MGO > DMO > PPSO > ASO. Moreover, a graphical user interface (GUI) was constructed based on the best estimation model that was built so that the shear strength of UHPC-Bs could be estimated in real-world situations without the need for any extra software or tools. This enables more users to quickly and easily estimate the shear strength of UHPC-Bs, optimize design processes, and decrease experimental testing costs.Öğe Experimental investigation and explainable artificial intelligence-based modeling of punching shear behavior in self-compacting concrete flat-slabs with low hybrid fiber content(Pergamon-Elsevier Science Ltd, 2026) Ari, Abdulkerim; Katlav, Metin; Donmez, Izzeddin; Turk, KazimFlat-slab systems manufactured with self-compacting concrete (SCC) incorporating low hybrid fiber content offer a promising alternative for improving punching shear performance while enhancing constructability in building applications. In this paper, the punching shear behavior of flat-slabs produced with single, binary, and ternary fiber-reinforced SCC was experimentally investigated in terms of load-deflection response, ductility, toughness, cracking behavior, and failure mode. In parallel, a comprehensive database comprising 268 fiber-reinforced concrete flat-slab test results collected from the literature was established, and artificial intelligence (AI)based predictive models were developed to estimate punching shear capacity (Vpun). Model performance was evaluated using statistical indicators, whereas SHapley Additive exPlanations (SHAP) feature importance and partial dependence plots (PDPs) were employed to enhance interpretability and reveal the governing parameters influencing punching capacity. The outcomes demonstrate that binary hybrid fiber systems provide the most effective enhancement in punching capacity and post-cracking performance, even at low fiber contents, outperforming conventional solutions such as shear studs. Among the developed AI models, the Extra Trees Regressor and Random Forest algorithms exhibited the highest prediction accuracy for the Vpun. Finally, the AI models were integrated into a user-friendly graphical interface to facilitate practical engineering applications. Overall, this research contributes by experimentally validating low-fiber SCC flat-slabs as an efficient punching solution and by proposing an explainable, data-driven decision-support framework for engineering design.Öğe Explainable artificial intelligence (XAI)-powered design framework for lightweight strain-hardening ultra-high-performance composites (SH-UHPC)(Wiley, 2026) Katlav, Metin; Turk, KazimLightweight strain-hardening ultra-high-performance concrete composite (SH-UHPC) is an outstanding alternative for engineering applications and infrastructure thanks to its outstanding strength, toughness, ductility, and low density. The integration of artificial intelligence (AI)-based modeling strategies into engineering problems can substantially accelerate material design processes while reducing experimental costs and time. Within this scope, the main motivation of this study is to predict the compressive strength (CS) of lightweight SH-UHPC via a grey wolf optimization (GWO)-integrated machine learning (ML)-based modeling that offers high accuracy and reliability, thereby reducing experimental cost and time requirements while supporting environmental and economic sustainability. The overall results demonstrate that all developed GWO-ML models achieved impressive performance levels in predicting the CS of lightweight SH-UHPC. In particular, the GWO-Extra Trees Regressor (GWO-ETR) model demonstrated superior performance compared to other GWO-ML models in terms of performance metrics (RMSE = 6.99, MAPE = 3.25%, and R-2 = 0.929), scatter plots (all points remained within a 10% margin of error), and uncertainty analysis (U-95 = 10.0) during the testing phase. In addition, SHapley Additive exPlanations-based feature importance analysis, as well as individual conditional expectation analysis and partial dependence plots, provided valuable insights and design suggestions for engineers and practitioners. Finally, an interactive graphical user interface has been developed to enable the application of similar data-driven analyses on a larger scale and to obtain rapid predictions; however, it is suggested that the database be continuously updated to improve model performance and extend its generalization capacity in the future.Öğe Explainable ensemble algorithms with grey wolf optimization for estimation of the tensile performance of polyethylene fiber-reinforced engineered cementitious composite(Elsevier, 2025) Tabar, Mehmet Emin; Katlav, Metin; Turk, KazimThis paper extensively examines the applicability of optimized ensemble machine learning (ML) algorithms via grey wolf optimization (GWO) to estimate the tensile performance of polyethylene fiber-reinforced engineered cementitious composites (PE-ECC). A robust and credible dataset is utilized for the establishment of the models based on available studies in the literature: The dataset includes 132 instances of PE-ECC mixes with 11 input features and 2 target output. Moreover, feature importance, Shapley additive explanation (SHAP) and partial dependence (PDP) analyses are implemented to enhance the explainability of the estimation models and to address the black box challenge of ML models. Based on the obtained results, the optimized extreme gradient boosting (XGBoost) and categorical boosting (CatBoost) models with GWO estimated the tensile performance of PE-ECC more effectively and accurately in comparison with other ensemble models. This has been extensively evaluated and proved through various approaches such as performance indicators, Taylor diagram, error analysis, and score analysis. To give a quantitative example, in the testing phase, for the prediction of tensile strain capacity, the GWO-XGBoost model reached the highest accuracy values with R2= 0.785 and RMSE= 1.077, whereas for the GWO-CatBoost model, these performance indicators were 0.764 and 1.129, respectively. In terms of tensile strength prediction, the GWO-XGBoost model achieved a high prediction accuracy with R2= 0.930 and RMSE= 1.004, while for the GWO-CatBoost model, R2 and RMSE were 0.932 and 0.987, respectively. Meanwhile, SHAP and PDP analyses were employed to identify the most influential features on output, and thus providing precious insight for designers to improve the tensile performance of PE-ECC. Additionally, a userfriendly graphical user interface (GUI) was constructed for estimating the tensile performance of PE-ECC and validated with new experimental datasets, illustrating the efficiency of the models. All in all, the importance of this work highlights the superior performance of the advanced GWO-ML models and GUI for estimating the tensile performance of PE-ECC and is thought to be a valuable contribution for further research in this area.Öğe Flexural performance of V-shaped RC folded plates: The role of plate thickness and fiber hybridization(Elsevier Sci Ltd, 2023) Katlav, Metin; Turk, Kazim; Turgut, PakiReinforced concrete folded plates (RC-FPs) are frequently used in structures such as industrial buildings, hangars, swimming pools, and sports halls due to their high load-bearing capacity, low self-weight, economic advantages, and architectural appearance. However, experimental studies on the reinforced concrete (RC) behavior of these new-generation structural members are very limited. For this purpose, this article investigated the effect of plate thickness and fiber hybridization on the flexural performance of V-shaped RC-FPs produced from self-compacting concrete (SCC). With this study, the experimental moment-curvature tool was used for the first time to evaluate the flexural performance of V-shaped RC-FP. A total of sixteen large-scale V-shaped RC-FP specimens with different plate thicknesses (50, 60, 70, and 80 mm) and fiber hybridization (single, binary, and ternary) were manufactured and subjected to a four-point loading after a 90-day curing period. After the experimental load-deflection and moment-curvature curves were obtained, load-bearing capacity, toughness, curvature ductility, and effective flexural stiffness values were calculated and also showed in the crack patterns for all large-scale V-shaped RC-FPs. The empirical equations with high-precision have been developed using multiple linear regression analysis for predicting the load-bearing capacity, toughness, curvature ductility, and effective flexural stiffness of V-shaped RC-FPs based on the parameters of plate thickness and fiber hybridization. Consequently, the use of hybrid fiber-reinforced SCC in the production of V-shaped RC-FPs exhibited superior properties in terms of flexural performance and crack behavior, as well as allowing for accelerated erection of the roof carrier system, resulting in significantly reducing construction time and costs. Also, fiber reinforcement rather than an increase in plate thickness induced significant increases in the flexural performance values of the V-shaped RC-FPs, while ternary fiber hybridization was the best.Öğe The impact of different length hooked-end fibers on the structural performance of RC folded plates(Ernst & Sohn, 2024) Katlav, Metin; Turk, Kazim; Turgut, PakiIn this article, the effect of hooked-end fibers with different lengths on the structural performance of RC-FPs fabricated from hybrid fiber-reinforced self-compacting concrete (HFR-SCC) was investigated. For this purpose, a total of 15 full-scale test samples having different plate thicknesses (60, 70, and 80 mm) were produced and tested under bending after a 90-day curing period. Subsequently, load-carrying capacity (P-p), flexural toughness (Fth), and deflection ductility index (mu(u)) of all RC-FPs were found using load-deflection curves obtained from bending tests, while crack patterns were drawn from the samples tested. Besides, high-precision contour plots are proposed to estimate the structural performance values of RC-FPs depending on plate thickness and fiber reinforcing index. As a result, the best structural performance in RC-FPs was obtained from the use of a longer hooked-end steel fiber together with micro steel fiber as a hybrid, followed by the lower length hooked-end steel fibers as singles. Specifically, irrespective of the plate thickness, the hybrid use of longer hooked steel fibers in combination with micro fibers increased the P-p, Fth, and mu(u) values of RC-FPs on average 1.67, 1.76, and 1.57 times, respectively, compared to the control specimens. As for when using the lower length hooked-end fiber as single, the values of P-p, Fth, and mu(u) increased on average 1.57, 1.69, and 1.30 times. Lastly, whereas plate thickness has little effect on improving the structural performance of thin-walled carrier elements such as RC-FPs, adding fibers in different lengths, aspect ratios, and combinations is much more effective. The collective test results demonstrate that using RC-FPs made of HFR-SCC in the roof carrier system of large span structures could improve structural performance, aesthetics, erection time, and earthquake behavior thanks to reduced dead load.Öğe Improved forecasting of the compressive strength of ultra-high-performance concrete (UHPC) via the CatBoost model optimized with different algorithms(Ernst & Sohn, 2024) Katlav, Metin; Ergen, FarukThis paper focuses on the applicability of CatBoost models constructed using various optimization techniques for improved forecasting the compressive strength of ultra-high-performance concrete (UHPC). Phasor particle swarm optimization (PPSO), dwarf mongoose optimization (DMO), and atom search optimization (ASO), which have been very popular recently, are preferred as optimization algorithms. A comprehensive and reliable data set is used to develop the CatBoost models, which include 785 test results with 15 input features. The performance of the CatBoost models (PPSO-CatBoost, DMO-CatBoost, and ASO-CatBoost) optimized with different algorithms is thoroughly assessed by means of various statistical metrics and error analysis to determine the model with the best forecasting capability, and this model is compared with the models obtained from previous studies. In addition, Shapley additive exPlanations (SHAP) analysis is used to ensure the interpretability of the forecasting models and to overcome the black box problem of machine learning (ML) models. The obtained results demonstrate that all CatBoost models outstandingly forecast the compressive strength of UHPC. Among these models, the DMO-CatBoost model stands out compared to the other models in various performance metrics, such as high coefficient of determination (R2) values, low root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) values, along with a smaller error ratio. In other words, the RMSE, R-2, MAPE, and MAE values of the DMO-CatBoost model for the training set are 3.67, 0.993, 0.019, and 2.35, respectively, whereas those for the test set are 6.15, 0.978, 0.038, and 4.51. Additionally, the performance ranking of the algorithms used to optimize the hyperparameters of the CatBoost model is as follows: DMO > PPSO > ASO. On the other hand, SHAP analysis showed that age, fiber dosage, and cement dosage significantly influence the compressive strength of UHPC. These findings can guide structural engineers in the design and optimization of UHPC, thus assisting them in developing strategies to improve the strength properties of the material. Finally, based on the best forecasting model developed in this work, a graphical user interface has been developed to easily forecast the compressive strength of UHPC in practical applications without additional tools or software.Öğe Improvement of fresh and hardened properties of a sustainable HFRSCC using various powders as multi-blended binders(Elsevier Sci Ltd, 2023) Donmez, Izzeddin; Katlav, Metin; Turk, KazimHybrid fiber-reinforced self-compacting concrete (HFRSCC) has been very popular in recent years due to its high mechanical, durability and flexural performance. HFRSCC properties are also closely related to its workability properties. Therefore, a high proportion of binder material is required to provide a uniform distribution of the fibers in the matrix. The use of mineral admixtures replaced by cement has a vital importance to improve especially the workability of HFRSCC mixtures, resulting in reduction of CO2 emission because the production of 1 ton Portland cement releases about 1 ton of CO2. For this reason, in this study, the effects of multi-blended (binary, ternary and quaternary) binders containing Portland Cement (PC), fly ash (FA), ground granulated blast furnace slag (BS) and limestone powder (LP) on the workability (Slump-flow, T500, J-ring and V-funnel) and hardened (compressive, splitting tensile and flexural tensile strength) properties of HFRSCC as well as flexural performance for 7, 28 and 90 days are investigated. The first of three group mixtures in this study consists of only control mixture (Control) without fiber and mineral admixture blends (MAB), in second group there are four SCC mixtures with only MAB replaced by cement as binary, ternary, quaternary SCC-MAB and the last group also includes four SCC mixtures with both mineral admixtures same as second group and hybrid fiber (HFRSCC-MAB). The total binder amount, water/binder ratio, fine aggregate/all aggregate ratio and fiber hybridization are kept constant while the mineral admixture type and blending system are variable parameters. According to the test results, among the HFRSCC-MAB blends, the quaternary blend system performed the best in terms of workability, followed by the binary blends containing FA. In addition, when it came to ultimate strengths, hybrid fiber -reinforced samples with ternary blends performed best for compressive strength, while hybrid fiber-reinforced samples with binary blends containing FA performed best for splitting tensile and flexural tensile strengths. Finally, it has been seen that the use of various powders as multi-blended binders is a successful solution to obtain high workability for uniform distribution of fibers in HFRSCC as well as high compressive strength and flexural performance, resulting in economical, eco-friendly and sustainable composite.Öğe Intelligent design framework for compressive strength modeling of ultra-high-performance geopolymer concrete (UHPGC): grey wolf optimizer-integrated machine learning and experimental verification(Taylor & Francis Ltd, 2026) Katlav, Metin; Turk, KazimUltra-high-performance concrete (UHPC) provides superior strength and durability but suffers from high cost and environmental impact. As a sustainable alternative, Ultra-High-Performance Geopolymer Concrete (UHPGC) requires reliable tools for predicting compressive strength (CS), yet existing frameworks remain limited, especially those combining AI, explainability, and experimental verification. This paper develops a Grey Wolf Optimizer (GWO)-enhanced machine learning framework to predict the CS of UHPGC using 179 mixes compiled from the literature. Four GWO-ML models (CatBoost, GBM, RF, ETR) were trained, with GWO-CatBoost achieving the highest performance (R2 = 0.971), followed by GWO-GBM (R2 = 0.967). SHAP-based analysis identified age, fiber, SF, SFL, and Na2SiO3 as the most influential variables. ICE and PDPs provided optimal design ranges for engineering use. A user-friendly GUI was also developed to predict CS along with cost and carbon footprint. Experimental tests on 10 new mixtures confirmed strong generalization of the GWO-CatBoost model (R2 = 0.884).Öğe Investigating the applicability of deep learning and machine learning models in predicting the structural performance of V-shaped RC folded plates(Elsevier, 2024) Katlav, Metin; Ergen, Faruk; Turk, Kazim; Turgut, PakiReinforced concrete folded plates (RC-FPs), which are a special class of shell structures, have recently become very popular in modern architectural and engineering applications because of the need for lightweight and aesthetic structures to cover large areas. However, it is known that studies on the structural performance of RCFPs are insufficient. Therefore, this article presents a study on the development and comparison of different deep learning (DL) and machine learning (ML) models for the prediction of the structural performance of full-scale Vshaped RC-FPs produced from hybrid fiber-reinforced self-consolidating concrete (HFR-SCC) having different plate thicknesses (50, 60, 70, and 80 mm), fiber volumes (1.00% and 1.25%), and combinations (single, binary, and ternary). While vanilla long short-term memory (VLSTM) and bidirectional long short-term memory (BILSTM) are used as DL models, random forest (RF), extremely randomized trees (ERT), and adaptive boosting (AdaBoost) are preferred for ML models. To construct the models, the structural performance results of a total of 44 full-scale V-shaped RC-FPs subjected to four-point bending loading were adopted as the database. In addition to all these, the AdaBoost model is used to determine the relative feature importance of the input parameters. Based on the results, among the DL models, the BILSTM has the best ability to predict the structural performance values of V-shaped RC-FPs (such as R-squared values for maximum load-bearing capacity, cracking load, toughness, and deflection ductility are 0.934, 0.987, 0.972, and 0.812, respectively), while in ML models, this is valid for the ERT (such as R-square values are 0.917 for maximum load-bearing capacity, 0.936 for cracking load, 0.947 for toughness and 0.825 for deflection ductility). On the other hand, DL models predicted all other structural performance values better than ML models, except for deflection ductility. Besides, the most relative important input features for maximum load-bearing capacity and toughness values is plate thickness, whereas for cracking load and deflection ductility values compressive strength is important. In conclusion, it can be emphasized that the use of DL models can provide significant advantages in engineering applications, such as predicting the structural performance of V-shaped RC-FPs.Öğe Investigation of optimized machine learning models with PSO for forecasting the shear capacity of steel fiber-reinforced SCC beams with/out stirrups(Elsevier, 2024) Ergen, Faruk; Katlav, MetinThis article presents a comprehensive investigation of the applicability of optimized machine learning (ML) models with particle swarm optimization (PSO) for forecasting the shear strength of steel fiber-reinforced self-compacting concrete (SFR-SCC) beams with/without stirrups in engineering applications. Firstly, a database containing the results of 101 specimens with nine input features is adopted to train the models. As ML models such as random forest (RF), adaptive boosting regression (AdaBoost), extreme gradient boosting (XGBoost), support vector regression (SVR), and K-nearest neighbors regression (KNN) are considered, whereas the hyper-parameters of these models are set as default by the sklearn module. On the other hand, PSO-ML models (PSO-RF, PSO-AdaBoost, PSO-XGBoost, PSO-SVR, and PSO-KNN) are constructed using particle swarm optimization to find the optimal combination of the hyper-parameters of these default ML models. Afterwards, the forecasting ability of each model is extensively assessed using various performance metrics, error analysis, and score analysis, and the model with the best forecasting ability is determined and compared with existing empirical models. Moreover, Shapley additive explanation (SHAP) analysis is also utilized to ensure the interpretability of the forecasting models and to overcome the black box problem of ML methods. Lastly, based on the best forecasting model developed in this study, a graphical user interface (GUI) has been developed to easily forecast the shear strength of SFR-SCC beams in practical applications. The results of the study clearly illustrate that PSO-ML models exhibit better forecasting capabilities than default models. It can be emphasized from here that the PSO algorithm can be an effective tool to improve the performance of ML models. It should also be pointed out that the use of PSO in simpler algorithms instead of tree-based models can further improve forecasting efficiency. On the other hand, the PSO-RF model has the best performance, with a lower error value and a high final score. And this makes it a more reliable option for predicting the shear strength of the SFRSCC beams compared to empirical equations. In addition, according to the results of SHAP feature importance analysis, the most important input parameters affecting the shear strength of SFR-SCC beams are stirrup rebar ratio (rho v), stirrup yield strength (fyv) and longitudinal rebar ratio (rho t). This information can assist engineers in paying special attention to these features in their design and assessment processes.Öğe KARMA ÇELİK LİFLİ KENDİLİĞİNDEN YERLEŞEN BETONUN ELEKTRİKSEL DİRENCİ(2022) Türk, Kazım; Çiçek, Nazlı; Katlav, Metin; Turğut, PakiBeton yüksek basınç dayanımı yanı sıra çok düşük elektriksel iletkenliği sahiptir. Bu çalışmada kendiliğinden yerleşen betonun (KYB) elektriksel özdirenci, iletkenliği ve sıcaklık artışı üzerinde uzun ve kısa çelik liflerin etkisini, lif kombinasyonu (tek ve karma) ve kısa çelik liflerin boyuna (6 ve 13 mm) bağlı olarak belirlemek için dört adet karışım tasarlanmıştır. Bu karışımlar, lifsiz referans, sadece uzun tek lif takviyeli ve uzun lif ile iki farklı boya sahip kısa çelik lif içeren iki adet karma çelik lif takviyeli karışım olmak üzere dört farklı karışım tasarlanmıştır. Tüm çelik lif takviyeli KYB karışımları hacimce toplam %1 lif içermektedir. Karışımların belirlenmesinde EFNARC tarafından önerilen işlenebilirlik testleri (Çökme-yayılma, t500 ve J-halkası yükseklik farkı) dikkate alınmıştır. Karışımlara ait mekanik özellikler (basınç, yarmada çekme ve eğilme dayanımı) ile elektriksel özdirencin belirlenmesi için numuneler üretilmiş ve toplam 90 gün boyunca 23±2 0C’de su içerisinde kür edilmiştir. Sonuçta çelik lif takviyesinin betonun elektriksel özdirencini düşürdüğü ve dolayısıyla iletkenliğini artırdığı tespit edilmiştir. Bunun yanında karma lifli KYB numunelerinin en düşük elektriksel özdirenç ve en yüksek iletkenlik ile sıcaklık artışına sahip olduğu görülürken, narinliği yüksek olan 13 mm boyunda mikro çelik lifin betonun elektriksel özellikleri üzerinde 6 mm boyunda mikro çelik life göre daha olumlu etkiye sahip olduğu bulunmuştur.
