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Öğe Bond strength of reinforcing bars in hybrid fiber-reinforced SCC with binary, ternary and quaternary blends of steel and PVA fibers(Springer, 2021) Kina, Ceren; Turk, KazimIn this study, the effect of inclusion of single fiber and binary, ternary and quaternary fiber hybridization on the bond performance of high strength self-compacting concrete (SCC) was investigated and 12 beam specimens having lap-spliced reinforcing bars in tension at the mid-span were designed. Four different fibers were used with different hybridizations. Fiber reinforced concrete beams demonstrated higher failure loads with a greater number of cracks. Especially the specimens with ternary fiber hybridization showed the best performance that the ultimate load resistance was 60% higher than that of the specimen without fiber. After splitting failure, the beam specimens with binary hybridization of macro steel fiber and polyvinyl-alcohol (PVA) fiber and also, the specimens with ternary hybridization of macro steel fiber, micro steel fiber with 13 mm in length (OL 13/.16) and PVA fiber showed a gradually drop in performance with increasing deflections. Besides, results indicate that the least improvement in bond strength was observed in the specimen having quaternary fiber hybridization of macro steel fiber, OL 13/.16 and micro steel fiber with 6 mm in length (OL 6/.16) and PVA fiber. The bond strength results were also compared with the ones calculated from the existing prediction equations. It was found that Zuo and Darwin and Esfahani and Rangan equations gave better results than the equations of Orangun et al. and ACI 318 on the hybrid fiber reinforced SCC. Based on the results, it was indicated that in these proposals, a new parameter was necessary for the fiber content so in this study, a new empirical equation was derived by using fiber reinforced index for fiber reinforced SCC. The proposed equation gave better estimation in the specimens with single fiber and binary and ternary fiber hybridization.Öğe Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC(Springer London Ltd, 2021) Kina, Ceren; Turk, Kazim; Atalay, Esma; Donmez, Izzeddin; Tanyildizi, HarunThis paper studied the estimation of fresh properties of hybrid fiber-reinforced self-compacting concrete (HR-SCC) mixtures with different types and combinations of fibers by using two different prediction method named as the methodologies of extreme learning machine and long short-term memory (LSTM). For this purpose, 48 mixtures, which were designed as single, binary, ternary and quaternary fiber-reinforced SCC with macro-steel fiber, two micro-steel fibers having different aspect ratio, polypropylene (PP) and polyvinylalcohol (PVA), were used. Slump flow, t(50) and J-ring tests for designed mixtures were conducted to measure the fresh properties of fiber-reinforced SCC mixtures as per EFNARC. The experimental results were analyzed by Anova method. In the devised prediction model, the amounts of cement, fly ash, silica fume, blast furnace slag, limestone powder, aggregate, water, high-range water-reducer admixture (HRWA) and the fiber ratios were selected as inputs, while the slump flow, t(50) and the J-ring were selected as outputs. Based on the Anova analysis' results, the macro-steel fiber was the most important parameter for the results of slump-flow diameter and t(50), while the most important parameter for the results of J-ring was fly ash. Furthermore, it was found that the use of more than 0.20% by volume of 6/0.16 micro-steel fiber positively influenced the fresh properties of SCC mixtures with hybrid fiber. On the other hand, the inclusion of steel fiber instead of synthetic fiber into SCC mixture as micro-fiber was more advantageous in terms of workability of mixtures as result of hydrophobic nature of steel fibers. This study found that extreme learning machine model estimated the slump flow, t(50) and J-ring with 99.71%, 81% and 94.21% accuracy, respectively, while deep learning model found the same experimental results with 99.18%, 77.4% and 84.8% accuracy, respectively. It can be emphasized from this study that the extreme learning machine model had a better prediction ability than the deep learning model.Öğe Deep learning and machine learning-based prediction of capillary water absorption of hybrid fiber reinforced self-compacting concrete(Ernst & Sohn, 2022) Kina, Ceren; Turk, Kazim; Tanyildizi, HarunDeep auto-encoders and long short-term memory methodology (LSTM) based on deep learning as well as support vector regression (SVR) and k-nearest neighbors (kNN) based on machine learning models for the capillary water absorption prediction of self-compacting concrete (SCC) with single and binary, ternary, and quaternary fiber hybridization were developed. A macro and two types of micro steel fibers having different aspect ratios, and PVA fiber were used. One hundred and sixty-eight specimens produced from 24 mixtures were used in the prediction models. The input was the content of cement, fly ash, silica fume, fine and coarse aggregate, water, superplasticizer (SP), macro and micro steel fibers, PVA, time that the specimen was immersed in water, and splitting tensile strength. Water absorption was used as output. As per the ANOVA analysis of the experiment results, the most effective parameters were macro steel fiber and time for tensile strength and water absorption, respectively. Finally, binary hybridization of 1% macro steel fiber and PVA improved the splitting tensile strength while the use of PVA as binary, ternary, and quaternary fiber hybridization increased the water absorption of SCC specimens. The auto-encoder, LSTM, SVR, and kNN models predicted the water absorption of fiber reinforced SCC with 99.99%, 99.80%, 94.57%, and 95.50% accuracy, respectively. The performance of deep autoencoder in the estimation of water absorption of fiber-reinforced SCC was superior to the other prediction models.Öğe Durability of Engineered Cementitious Composites Incorporating High-Volume Fly Ash and Limestone Powder(Mdpi, 2022) Turk, Kazim; Kina, Ceren; Nehdi, Moncef L.This study investigates the effects of using limestone powder (LSP) and high-volume fly ash (FA) as partial replacement for silica sand (SS) and portland cement (PC), respectively, on the durability properties of sustainable engineered cementitious composites (ECC). The mixture design of ECC included FA/PC ratio of 1.2, 2.2 and 3.2, while LSP was used at 0%, 50% and 100% of SS by mass for each FA/PC ratio. Freeze-thaw and rapid chloride ions penetrability (RCPT) tests were performed to assess the durability properties of ECC, while the compressive and flexural strength tests were carried out to appraise the mechanical properties. Moreover, mercury intrusion porosimetry (MIP) tests were performed to characterize the pore structure of ECC and to associate porosity with the relative dynamic modulus of elasticity, RCPT and mechanical strengths. It was found that using FA/PC ratio of more than 1.2 worsened both the mechanical and durability properties of ECC. Replacement of LSP for SS enhanced both mechanical strengths and durability characteristics of ECC, owing to refined pore size distribution caused by the microfiller effect. It can be further inferred from MIP test results that the total porosity had a vital effect on the resistance to freezing-thawing cycles and chloride ions penetration in sustainable ECC.Öğe Effect of macro and micro fiber volume on the flexural performance of hybrid fiber reinforced SCC(Techno-Press, 2020) Turk, Kazim; Kina, Ceren; Oztekin, ErolThe aim of this study is to investigate the flexural performance of hybrid fiber reinforced self-compacting concrete (HFRSCC) having different ratio of micro and macro steel fiber. A total of five mixtures are prepared. In all mixtures, the sum of the steel fiber content is 1% and also water/binder ratio is kept constant. The amount of high range water reducer admixture (HRWRA) is arranged to satisfy the workability criteria of self-compacting concrete. Four-point bending test is carried out to analyze the flexural performance of the mixtures at 28 and 56 curing days. From the obtained load-deflection curves, the load carrying capacity, deflection and toughness values are investigated according to ASTM C1609, ASTM C1018 and JSCE standards. The mixtures containing higher ratio of macro steel fiber exhibit numerous micro-cracks and, thus, deflection-hardening response is observed. The mixture containing 1% micro steel fiber shows worst performance in the view of all flexural parameters. An improvement is observed in the aspect of toughness and load carrying capacity as the macro steel fiber content increases. The test results based on the standards are also compared taking account of abovementioned standards.Öğe Estimation of strengths of hybrid FR-SCC by using deep-learning and support vector regression models(Ernst & Sohn, 2022) Kina, Ceren; Turk, Kazim; Tanyildizi, HarunIn this work, to estimate the compressive, splitting tensile, and flexural strength of self-compacting concrete (SCC) having single fiber and binary, ternary, and quaternary fiber hybridization, the deep-learning (DL) and support vector regression (SVR) models were devised. The fiber content and coarse aggregate/total aggregate ratio (CA/TA) were the variables for 24 designed mixtures. Four different fibers, which were a macro steel fiber, two types of micro steel fibers with different aspect ratio, and polyvinyl alcohol (PVA) fiber, were used in SCC mixtures. The specimens of each mixture were tested to measure the engineering properties for 7, 28, and 90 days. The amount of cement, fly ash, fine aggregate, CA, high-range water-reducing admixture, water, macro steel fiber, PVA fiber, two types of micro steel fibers, and curing time were selected as input layers while the output layers were strength results. The experimental results were compared with the estimation results. The engineering properties were estimated using the SVR model with 95.25%, 87.81%, and 93.89% accuracy, respectively. Furthermore, the DL model estimated compressive strength, tensile strength, and flexural strength with 99.27%, 98.59%, and 99.15% accuracy, respectively. It was found that the DL estimated the engineering properties of hybrid fiber-reinforced SCC with higher accuracy than SVR.Öğe Extreme Learning Machine for Estimation of the Engineering Properties of Self-Compacting Mortar with High-Volume Mineral Admixtures(Springer Int Publ Ag, 2024) Turk, Kazim; Kina, Ceren; Tanyildizi, HarunThe utilization of supplementary cementitious materials obtained from industrial by-products or wastes is one of the most effective ways to minimize the costs as well as environmental impact associated with cement production. This work investigated the effects of the replacement of Portland cement (PC) with (25, 30, 35 and 40%) fly ash (FA) and (5, 10, 15, and 20%) silica fume (SF) by weight as binary and ternary blends on the compressive strength (f(c)) and flexural strength (f(ft)) of self-compacting mortars (SCMs) at 28 and 91 curing days. Extreme learning machine (ELM), support vector regression (SVR), artificial neural network (ANN), and decision tree (DT) models were devised to predict these strengths of SCMs containing high-volume mineral admixture (HVMA). The selected input variables were the number of curing days, water-cementitious material (W/CM), PC, FA, SF, and sand contents, while the f(c) and f(ft) were the output variables. ANOVA results show that the curing time was the most effective parameter for determining both strengths. The results also indicated that ELM achieved superior performance for the prediction of f(c) and f(ft) of SCMs with HVMA compared to SVR, ANN, and DT due to having the highest coefficient of determination values of 0.9802 for both strengths.Öğe Fire resistance of hybrid fiber reinforced SCC: Effect of use of polyvinyl-alcohol or polypropylene with single and binary steel fiber(Techno-Press, 2023) Turk, Kazim; Kina, Ceren; Balalan, EsmaThis study presents the experimental results performed to evaluate the effects of Polyvinyl-alcohol (PVA) and Polypropylene (PP) fibers on the fresh and residual mechanical properties of the hybrid fiber reinforced SCC before and after the exposure of 250 & DEG;C, 500 & DEG;C and 750 & DEG;C temperatures. The compressive and splitting tensile strength, modulus of rupture (MOR), ultrasonic pulse velocity (UPV) as well as toughness and weight loss were investigated at different temperatures. PVA and PP fibers were added into SCC mixtures having only macro steel fiber and also having binary hybridization of both macro and micro steel fiber. The results showed that the use of micro steel fiber replaced by macro steel fiber improved the fresh and hardened properties compared to the use of only macro steel fiber. Moreover, it was emphasized that PVA or PP enhanced the residual flexural performance of SCC, generally, while it negatively influenced the workability, weight loss, UPV and the residual strengths with regards to the use of single steel fiber and binary steel fiber hybridization. Compared to the effect of synthetic fibers, PP had slightly more positive effect in the view of workability while PVA enhanced the residual mechanical properties more.Öğe Forecasting the compressive strength of GGBFS-based geopolymer concrete via ensemble predictive models(Elsevier Sci Ltd, 2023) Kina, Ceren; Tanyildizi, Harun; Turk, KazimThe compressive strength (fc) of the concrete is an important parameter in the structural design. However, the assessment of fc via an experimental program is time-consuming, costly, and needs a labor force. Therefore, the forecasting of fc through different algorithms can accelerate and facilitate this process and also provide guidance for scheduling the progress of the construction. While some studies have explored the use of models for the prediction of fc of concrete, the ensemble models that can predict the fc of GPC with industrial by-products is still lacking. Within this scope, decision tree (DT), Bootstrap aggregating (Bagging), and Least-squares boosting (LSBoost) models were devised to predict fc of ground granulated blast furnace slag (GGBFS)-based geopolymer concrete (GPC). The data points collected to devise a GEP model in the previous study were used and the prediction results of the GEP model were compared with the proposed ensemble models in the current study. The age of the specimen, NaOH solution concentration, natural zeolite (NZ) content, silica fume (SF) content, and GGBFS content were used as input parameters, and fc was used as output parameter. According to ANOVA analysis, the age of the specimen was found as the most influential parameter in the determination of the fc of GGBFS-based GPC. Also, Multiple linear regression equation was proposed to estimate the fc of GGBFS-based GPC with the accuracy of 93%. The most accurate model was introduced through performance metrics and the Taylor diagram. The results proved that the highest accuracy and stable predictions were achieved by the LSBoost model with R-squared value of 98.25% followed by GEP model developed in the previous study, DT and Bagging models. However, it is worth mentioning that due to having a high coefficient of correlation values (>%80), DT and Bagging models also have an acceptable ability for predicting fc of GGBS-based GPC.Öğe Freeze-thaw resistance and sorptivity of self-compacting mortar with ternary blends(Techno-Press, 2018) Turk, Kazim; Kina, CerenThis paper investigated the influence of binary and ternary blends of mineral admixtures in self-compacted mortar (SCM) on the fresh, mechanical and durability properties. For this purpose, 25 mortar mixtures were prepared having a total binder content of 640 kg/cm(3) and water/binder ratio between 0.41 and 0.50. All the mixtures consisted of Portland cement (PC), fly ash (FA) and silica fume (SF) as binary and ternary blends and air-entrained admixture wasn't used while control mixture contained only PC. The compressive and tensile strength tests were conducted for 28 and 91 days as well as slump-flow and V-funnel time tests whilst freeze-thaw (F-T) resistance and capillary water absorption tests were made for 91-day. Finally, in general, the use of SF with FA as ternary blends improved the tensile strength of mortars at 28- and 91-day while the use of SF 15 with FA increased the compressive strength of the mortars compared to binary blends of FA. SCM mixtures with ternary blends had lower the sorptivity values than that of the mortars with binary blends of FA and the control mixture due to the beneficial properties of SF while the use of FA with SF as ternary blends induced the F-T resistance enhancement.Öğe Influence of Silica Fume and Class F Fly Ash on Mechanical and Rheological Properties and Freeze-Thaw Durability of Self-Compacting Mortars(Asce-Amer Soc Civil Engineers, 2018) Benli, Ahmet; Turk, Kazim; Kina, CerenThis paper investigates the effect of different dosages of fly ash (FA) and silica fume (SF) in self-compacting mortars (SCMs) on the freeze-thaw (F-T) resistance as well as fresh and hardened properties. Nine mortar mixtures were prepared at 640kg/cm3 of unit weight and water/binder (w/b) ratio between 0.43 and 0.50. All SCMs contained either FA or SF, but the control mixture contained only portland cement. The fresh properties of SCMs were determined using viscosity, slump flow, and V-funnel flow time tests. The compressive strength of SCMs containing SF was generally found to be higher than that of SCMs with FA and the control mixture. At 28 and 91days, the control mixture had the highest flexural tensile strength in all blends except SF20 at 91days. In addition, with increasing replacement rates of FA and SF in SCMs, the sorptivity of SCMs with FA resulted in an increase, whereas a reduction in the sorptivity of SCMs with SF was observed. Finally, as replacement ratio of FA and SF increased, the relative dynamic elastic modulus of SCMs showed a reduction, and the decreases in the elastic modulus of SCMs containing SF occurred sharply compared with SCMs incorporating FA because of the deleterious effects of SF on F-T cycles. Moreover, as SF and FA content increased, the dynamic elastic modulus of SCMs decreased at 28days.Öğe Machine Learning Prediction of Residual Mechanical Strength of Hybrid-Fiber-Reinforced Self-consolidating Concrete Exposed to Elevated Temperature(Springer, 2023) Turk, Kazim; Kina, Ceren; Tanyildizi, Harun; Balalan, Esma; Nehdi, Moncef L. L.Establishing the engineering properties of cement-based composites at elevated temperature requires costly, laborious, and time-consuming experimental work. Data-driven models can provide a robust and efficient alternative. In this study, extreme learning machine (ELM), support vector machine (SVM), artificial neural network (ANN), and decision tree (DT) models were trained to predict the residual compressive, splitting tensile, and flexural strengths of hybrid fiber-reinforced self-compacting concrete (HFR-SCC) exposed to high temperatures. Mixtures including macro and micro steel fibers, polyvinyl alcohol (PVA), and polypropylene (PP) were subjected to different temperature levels, leading to an experimental database of 360 specimens. Eleven input parameters including cement, fly ash, water, sand, gravel, fiber type, water reducer, and temperature were deployed. The residual mechanical strengths were targeted as output parameters. ANOVA was used to explore the influence of input parameters. Temperature was found to be the most influential parameter. Dataset consisting of 114 instances was retrieved from pertinent literature and used along with the authors' experimentally generated dataset for residual strength prediction. The experimental results were compared with predictions of ELM, SVM, ANN, and DT. ELM achieved superior performance and can offer a robust tool for predicting the residual mechanical strengths of HFR-SCC upon exposure to high temperature.Öğe Predicting sorptivity and freeze-thaw resistance of self-compacting mortar by using deep learning and k-nearest neighbor(Techno-Press, 2022) Turk, Kazi; Kina, Ceren; Tanyildizi, HarunIn this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze -thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.Öğe Self-compacting concrete with blended short and long fibres: experimental investigation on the role of fibre blend proportion(Taylor & Francis Ltd, 2022) Turk, Kazim; Oztekin, Erol; Kina, CerenIn this paper, micro and macro steel fibres were used to understand the influences of blended fibre addition on the fresh and hardened properties of self-compacting concrete (SCC). Five mixtures containing micro and macro steel fibres were prepared in different combinations and in each mixture, the sum of the steel fibre content was kept constant as 1%. To measure the workability of fibre-reinforced self-compacting concrete (FR-SCC), slump-flow diameter, t(500) and J-ring tests were conducted. The results indicated that when the micro steel fibre ratio became more than 0.25%, the fresh properties of SCC were affected negatively. To determine the hardened properties of FR-SCC mixtures, compressive, splitting tensile and flexural tensile strength tests were performed and also ultrasonic pulse velocity (UPV) of the specimens was measured. The micro steel fibre inclusion had positive effect on compressive strength while it caused a reduction in splitting tensile and flexural tensile strength. With regard to the crack formation, in the mixtures having higher content of macro steel fibre, multiple crack behaviour was observed. Moreover, it was revealed from the results that the mixtures exhibited deflection-hardening response in the case of inclusion higher amount of macro steel fibre.Öğe Use of binary and ternary cementitious blends of F-Class fly-ash and limestone powder to mitigate alkali-silica reaction risk(Elsevier Sci Ltd, 2017) Turk, Kazim; Kina, Ceren; Bagdiken, MahmutThe aim of this paper was to investigate the effects of binary and ternary cementitious systems including Portland Cement (PC), F-Class fly ash (FA) and limestone powder (LSP) to suppress expansion caused by alkali-silica reaction (ASR). Mortar prisms were prepared with potentially deleterious aggregates and tested by using Mortar-Bar Method according to ASTM C 1260. To evaluate the effect of FA and LSP on the suppression of ASR in mortar, ten different types of mortar were cast with combination of different dosages of FA and LSP as binary and ternary blend systems (partial replacement of PC) while the control mortar consisted of only PC as binder. Moreover, the Scanning Electron Microscopy (SEM) was carried out to interpret the microstructure. It was found that the reduction of ASR expansion rate due to increase in LSP content was more prominent compared to the increase of FA content in the binary blends system. Finally, the mixture containing ternary blends of 20% FA/LSP were in general more effective to mitigate the ASR risk for 14 days compared to all mortars. (C) 2017 Elsevier Ltd. All rights reserved.Öğe The use of hybrid in cementitious composites(Pamukkale Univ, 2017) Turk, Kazim; Kina, CerenWith the developments in the construction sector, the use of fiber in concrete technology has gained great importance due to the developments of the spread of high rise concrete buildings, the increasing importance given to infrastructure, investments in nuclear energy field. Therefore, it is clear that this fiber reinforced composite especially should have also ductility properties. Fiber reinforced composites are designed in order to prevent extremely structural collapse by absorbing massive amount of energy during extreme load-displacement events such as earthquakes, projectile impacts and explosions. It will be much more possible with using this composite in the construction sector to construct much higher concrete building, increase the economic life and strength of important infrastructure members, construct more secure containment buildings which is important for the safety of nuclear power plant, reduce the section and reinforcement ratios in reinforced concrete structural members. In this regard, in the concrete technology in order to provide the above mentioned composite properties, the significance of fiber usage has increased. It was found from studies made that it is possible to produce more developed composites using hybrid fiber having different properties with single or different fiber combinations. In this paper, the significance of the use of fiber for producing the cementitious composite, fiber types and the using forms (single or hybrid) and the effects of micro and macro fiber types on the engineering properties of the hybrid fiber reinforced composites are investigated. Finally, at construction sector the use of hybrid reinforced composites has gained great importance due to the formation of multiple crack and high tensile strength.
