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    The effect of caffeine molecule on the physico-chemical properties of blended cement
    (Elsevier Sci Ltd, 2020) Kurtay, Mine; Gerengi, Husnu; Kocak, Yilmaz
    Caffeine is a nontoxic and eco-friendly natural molecule used in the food industry as well as a corrosion inhibitor in various corrosive media. However, its behavior and performance as an additive in the construction industry has not been explored. This paper investigates the effect of caffeine as an additive on strength, water demands, setting time and hydration mechanism of cement. In the first instance, the water demand, volume expansion and setting time of cement pastes, incorporating 0, 25, 50 and 75 ppm of caffeine were determined. In the second section, the rate of hydration and cement pastes' products were conducted by means of X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), thermal analysis and scanning electron microscope (SEM) at 28 days. Finally, the cement mortars' compressive strengths were determined at 2, 7 and 28 days intervals. Consequently, mortars containing 75 ppm caffeine registered higher strength than the pure cement mortars due to high quantities of primary C-S-H gel, chemically bound water, compact structure at 28 days. Furthermore, by result of analysis and experiments, it was determined that caffeine had no negative effect on cement. (C) 2020 Elsevier Ltd. All rights reserved.
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    Effects of metakaolin on the hydration development of Portland-composite cement
    (Elsevier, 2020) Kocak, Yilmaz
    The hydration reactions of the cement are very complex and there is no single method that determines these reactions. For this reason, a few supplement techniques for various properties and hydration reactions of Portland-composite cement, which are silica fly ash and limestone, containing metakaolin were used in present study. In the first step, the physical, chemical, mineralogical, molecular characterizations of the metakaolin and Portland-composite cement (silica fly ash and limestone) were carried out. In the second step, physical properties and mechanical behavior of Portland-composite cements with metakaolin substituted in ratios of 0, 5, 10, 15 and 20 wt% of the total amount of PCC were determined. At the final stage, hydration reactions of metakaolin-substituted pastes are analyzed by the spectroscopic methods, which are X-ray diffraction, Fourier transform infrared spectroscopy and scanning electron microscopy, at 28 days. As a result, as the rate of substitution of metakaolin increases, the amount of portlandite which released during hydration, was decreased. In addition, the physical properties and mechanical behavior of Portland-composite cement and metakaolin-substituted paste samples were influenced by the amount of metakaolin.
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    Efficient machine learning models for estimation of compressive strengths of zeolite and diatomite substituting concrete in sodium chloride solution
    (Springer Heidelberg, 2024) Ozcan, Giyasettin; Kocak, Burak; Gulbandilar, Eyyup; Kocak, Yilmaz
    This study implements a set of machine learning algorithms to building material science, which predict the compressive strength of zeolite and diatomite substituting concrete mixes in sodium chloride solution. Particularly, Random Forest, Support Vector Machine, Extreme Gradient Boosting, Light Gradient Boosting, and Categorical Boosting algorithms are exploited and their optimal parameters are tuned. In the training and testing of these models, 28 day, 56 day, and 90 day compressive strength observations of 63 samples of 7 different concrete mixtures substituting Portland cement, zeolite, diatomite, zeolite + diatomite were used. Consequently, compressive strength experimentation results and machine learning predictions were compared through statistical methods such as RMSE, MAPE, and R 2. Results denote that the prediction performance of machine learning is improving with tuned models. Particularly, RMSE, MAPE, R 2 scores of Categorical Boosting are, respectively, 1.15, 1.45%, and 98.03% after parameter tuning design. The results denote that presented machine learning model can provide an advantage in the cost and duration of the compressive strength experiments.
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    The potency of zeolite and diatomite on the corrosive destruction of reinforcing steel in 1 M HNO3 environment
    (Elsevier Sci Ltd, 2020) Kurtay, Mine; Gerengi, Husnu; Kocak, Yilmaz; Chidiebere, Maduabuchi A.; Yildiz, Mesut
    Considering the high neutralizing power of concrete, it is seen as the major material for preserving reinforcing steel. This is observed in reinforced concrete (RC) formation. It is obvious that under corrosive situations, concrete is subject to noticeable quality losses. The aim of this research is to conduct an inquiry on the disintegration of reinforcing steel subjected to 1 M HNO3 solution over 200 days. For this purpose, concrete samples were produced having three different formulations, which included the reference (pure Portland cement), 20% diatomite and 20% zeolite. Reinforcing steel was embedded into these concrete samples. Afterwards, the electrochemical impedance spectroscopy (EIS) approach was used to monitor the system every seven days. The results clearly revealed that long-term experiments are required for accurate electrochemical measurements. The addition of diatomite and zeolite protected the reinforcement better against corrosion. Although it loses its effectiveness over time, zeolite provided better resistance against corrosion than diatomite for the reinforcement bars in 1 M HNO3 solution. (C) 2019 Elsevier Ltd. All rights reserved.
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    Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications
    (Elsevier Sci Ltd, 2023) Kocak, Burak; Pinarci, Brahim; Guvenc, Ugur; Kocak, Yilmaz
    In this study, two different Artificial neural networks (ANN) and two different adaptive network-based fuzzy inference systems (ANFIS) models were constructed to predict the compressive strength of 7 different cement mortar samples with or without pumice and/or diatomite on different days. Five parameters including day, PC, pumice, diatomite and water were employed as the inputs, and the compressive strength was used as the output variable. The compressive strengths used in the model construction were obtained from laboratory experiments accounting for a total of 168 data. Statistical methods such as R2, RMS and MAPE preferred in the literature were used to compare the four different models. According to the test results obtained from R2, RMS and MAPE, ANN and ANFIS models were able to make very good predictions performance. For this reason, it can be said that these cement mortars' compressive strength can be estimated with a very small error and in a short time with both ANN and ANFIS models.