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    Consumer preferences for flooring in Turkey in terms of purchasing and use
    (Inst Technol Drewna, 2016) Akyüz, İlker; Ersen, Nadir; Tiryaki, Sebahattin
    In this study, consumer preferences for flooring in terms of purchasing and use, were investigated, as well as consumer attitudes and the reasons for preferences for a particular product, and whether consumer behaviour varied according to gender; age, level of education, occupation and size of household. The study was based on a face-to-face interview of 1005 people throughout Turkey. The data obtained from the survey was analyzed using the statistical package SPSS 19.0 for Windows. According to the results of the study, it is possible to say that the customers preferred flooring which was easy to assemble, with good heat and sound insulation, resistant to physical and mechanical damage, both environmentally- and human-friendlyand aesthetically pleasing. Manufacturers should therefore endeavour to meet these expectations by obtaining positive and negative feedback from users of flooring. In conclusion, it was determined that flooring preference, usage, expectations and consumer complaints may differ according to gender, age, level of education, income level, household size and occupation. This study fills an important gap regarding the investigation of consumer preferences for flooring in terms of purchasing and use in Turkey.
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    Experimental investigation and prediction of bonding strength of Oriental beech (Fagus orientalis Lipsky) bonded with polyvinyl acetate adhesive
    (Taylor & Francis Inc, 2015) Tiryaki, Sebahattin; Bardak, Selahattin; Bardak, Timuçin
    Adhesive bond strength of solid wood plays a key role in the efficient use of wood in a large number of engineering applications. In this study, the effects of amount of adhesive, pressing pressure, and pressing time on bonding strength of beech wood bonded with polyvinyl acetate adhesive were investigated and predicted by developing an artificial neural network (ANN) model. Experimental results have showed that bonding strength of wood samples increased generally by increasing amount of adhesive, pressing pressure, and pressing time. Besides, ANN analysis has yielded highly satisfactory results. The designed neural network model allows predicting the bonding strength of wood samples with mean absolute percentage error of 2.454% and correlation coefficient of 97.8% for testing phase. It is clear from the results that the model has a good learning and generalization ability. This model therefore can be used to predict bonding strength of beech samples bonded with polyvinyl acetate adhesive under given conditions. Consequently, this study provides beneficial insights for practitioners in terms of the safe and efficient use of wood as an engineering material in applications related to the strength of the bond between wood and adhesive.
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    Modeling and comparison of bonding strength of impregnated wood material by using different methods: Artifıcial neural network and multiple linear regression
    (Slovak Forest Products Research Inst, 2019) Akyüz, İlker; Ersen, Nadir; Tiryaki, Sebahattin; Bayram, Bahadır Çağrı; Akyüz, Kadri Cemil; Peker, Hüseyin
    In this study, the effects of vacuum time, diffusion time and pressing time on the bonding strength of Larix decidua wood impregnated with Immersol-Aqua and bonded with Klebit-303 were investigated. The vacuum time, diffusion time, and pressing time were predicted by using the artificial neural network (ANN) model and multiple linear regression (MLR) methods and the results of ANN and MLR methods were compared. The highest bonding strength (7.664 N.mm(-2)) was achieved when the vacuum time, the diffusion time and the pressing time were 20, 60 and 60 minutes, respectively, while the lowest value (4.62 N.mm(-2)) was achieved when the vacuum time, the diffusion time and the pressing time were 80, 120 and 20 minutes, respectively. The model results are as follows: The MAPE value for testing phase in the ANN was 7.266 and R-2 value was 0.751 whereas the MAPE value of the MLR was 9.365 and R-2 value was 0.558. The ANN model has been found to have better prediction performance than the MLR model.
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    Performance evaluation of multiple adaptive regression splines, teaching-learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood
    (Springer, 2019) Tiryaki, Sebahattin; Tan, Hüseyin; Bardak, Selahattin; Kankal, Murat; Nacar, Sinan; Peker, Hüseyin
    Understanding the mechanical behaviour of impregnated wood is crucial in making a preliminary decision on the usability of such woods for structural purposes. In this paper, by considering concentration (1, 3 and 5%), pressure (1, 1.5 and 2 atm.), and time (30, 60, 90 and 120 min), an experimental study was performed, and the mechanical behaviour of impregnated wood was determined as a result of the experimental process. Multiple adaptive regression splines (MARS), teaching–learning based optimization (TLBO) algorithms and conventional regression analysis (CRA) were applied to diferent regression functions by using experimentally obtained data. The functions were checked against each other to detect the best equation for each parameter and to assess performances of MARS, TLBO and CRA methods in the prediction of mechanical properties. The experimental results showed that higher values of mechanical properties were obtained when lower concentration, pressure and time were chosen. Overall, all the functions successfully predicted the mechanical properties. However, the MARS and TLBO provided better accuracy in predicting the mechanical properties. The modeling results indicated that the MARS and TLBO are promising new methods in predicting the mechanical properties of impregnated wood. With the use of these methods, the mechanical behavior of impregnated wood could be determined with high levels of accuracy. Thus, the proposed methods may facilitate a preliminary decision concerning the usability of such woods for areas where the mechanical properties are important. Finally, the employment of MARS and TLBO algorithms by practitioners in the wood industry is encouraged and recommended for future studies.