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    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, Harun
    This 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.
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    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, Paki
    Recently, 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.
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    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, Kazim
    Hybrid 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.