Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Publisher: search.filters.publisher.Springer VerlagSubject: search.filters.subject.Fly ash

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  • 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.
  • Article
    Citation - WoS: 75
    Citation - Scopus: 87
    Use of Spent Foundry Sand and Fly Ash for the Development of Green Self-Consolidating Concrete
    (Springer Verlag, 2011) Şahmaran, Mustafa; Lachemi, Mohamed; Erdem, Tahir Kemal; Yücel, Hasan Erhan
    In the United States alone, the foundry industry discards up to 10 million tons of sand each year, offering up a plentiful potential resource to replace sand in concrete products. However, because the use of spent foundry sand (SFS) is currently very limited in the concrete industry, this study investigates whether SFS can successfully be used as a sand replacement material in cost-effective, green, self-consolidating concrete (SCC). In the study, SCC mixtures were developed to be even more inexpensive and environmentally friendly by incorporating Portland cement with fly ash (FA). Tests done on SCC mixtures to determine fresh properties (slump flow diameter, slump flow time, V-funnel flow time, yield stress, and relative viscosity), compressive strength, drying shrinkage and transport properties (rapid chloride permeability and volume of permeable pores) show that replacing up to 100% of sand with SFS and up to 70% Portland cement with FA enables the manufacture of green, lower cost SCC mixtures with proper fresh, mechanical and durability properties. The beneficial effects of FA compensate for some possible detrimental effects of SFS.