Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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COAR Access Rights: search.filters.coarAccessRights.metadata only accessInstitution Author: search.filters.institutionAuthor.Erdem, Tahir Kemal

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  • Article
    Citation - WoS: 10
    Citation - Scopus: 10
    Effects of Mixture Design Parameters on the Mechanical Behavior of High-Performance Fiber-Reinforced Concretes
    (American Society of Civil Engineers (ASCE), 2020) Erdem, Tahir Kemal; Demirhan, Serhat; Yıldırım, Gürkan; Banyhussan, Qais S.; Şahin, Oğuzhan; Balav, Mohammad H.; Şahmaran, Mustafa
    The main purpose of this research is to assess the influence of different design parameters on the mechanical performance of high-performance fiber-reinforced concrete (HPFRC) mixtures. Special attention is also paid to achieving deflection-hardening behavior in the presence of a large amount of coarse aggregates. Different mixture design parameters were the initial curing ages (3, 7, 28, and 90 days), ratios of Class F fly ash (FA) to portland cement (PC) (0.0, 0.2, and 0.4), addition/type of nanomaterials [nanosilica (NS), nanoalumina (NA), and nanocalcite (NC)], and combinations of fibers [polyvinyl-alcohol + steel (P, S) or brass-coated microsteel + steel (B, S)]. The experimental program included the evaluation of compressive strength, flexural strength, and midspan deflection results in addition to test parameters recorded under biaxial flexural loading via a series of square panel tests, including peak load and energy absorption capacities. Test results revealed that deflection-hardening response coupled with multiple microcracks can be obtained when large amounts of coarse aggregates are available for all HPFRC mixtures. As expected, experimental results change depending on the different curing ages and FA/PC ratios. The most distinctive parameters affecting the results are addition/type of nanomaterials and the presence of different fiber combinations. In the presence of nanomaterials, all results from the different tests improved, especially for NA and NS inclusions. With slight concessions in flexural deflection results, B fiber is shown to be a successful candidate to fully replace costly P fibers because most properties of B, S fiber-reinforced HPFRC mixtures outperformed those with P, S fibers, both under four-point bending and biaxial flexural loading.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Generalized Regression Neural Network and Empirical Models To Predict the Strength of Gypsum Pastes Containing Fly Ash and Blast Furnace Slag
    (Springer Verlag, 2020) Erdem, Tahir Kemal; Cengiz, Okan; Tayfur, Gökmen
    Gypsum is widely used in constructions owing to its easy application, zero shrinkage, and excellent fire resistance. Several parameters can affect the properties of gypsum pastes. To study the strength of the gypsum pastes experimentally by trying all these parameters is time-consuming and costly. Therefore, artificial intelligence methods can be very useful to predict the paste strength, which, in turn, can reduce the number of trial batches. Based on experimental data, the generalized regression neural network (GRNN) and empirical models were developed to predict strength of gypsum pastes containing fly ash (FA) and blast furnace slag (BFS). Gypsum content, pozzolan content, curing temperature, curing duration, and testing age constituted the input variables of the models while the paste strength was the target output. The trained and tested GRNN model was found to be successful in predicting strength. Sensitivity analysis by the GRNN model revealed that the curing duration and temperature were important sensitive parameters. In addition to the GRNN model, empirical models were proposed for the strength prediction. The same input variables formed the input vectors of the empirical models. The same dataset used for the calibration of the GRNN model was employed to establish the empirical models by employing genetic algorithm (GA) method. The empirical models were successfully validated. The GRNN and GA_based empirical models were also tested against the multi-linear regression (MLR) and multi-nonlinear regression (MNLR) models. The results showed the outperformance of the GRNN and the GA_based empirical models over the others.