یک مدل شبکه عصبی مصنوعی برای پیش بینی مقاومت فشاری از چوب گرمای تحت درمان و مقایسه آن با یک مدل رگرسیون خطی چندگانه

This paper aims to design an artificial neural network model to predict compression strength parallel to grain of heat treated woods, without doing comprehensive experiments. In this study, the artificial neural network results were also compared with multiple linear regression results. The results indicated that artificial neural network model provided better prediction results compared to the multiple linear regression model. Thanks to the results of this study, strength properties of heat treated woods can be determined in a short period of time with low error rates so that usability of such wood species for structural purposes can be better understood.

Heat treatment is one of the processes used to improve the various properties of wood [1]. It was reported that heat treatment improves wood properties such as wood durability, dimensional stability and resistance to fungi [2] and [3]. However, it is a fact that increased temperature and duration during heat treatment adversely influence most of mechanical characteristics of wood [4], [5] and [6]. The temperature and duration of heat treatment generally varies from 120 °C to 250 °C and 15 min to 24 h, respectively depending on wood species, sample dimensions, moisture content of the sample and intended use [7]. Especially, temperatures over 150 °C applied to wood modify the mechanical, chemical and physical properties of wood gradually [8]. Wood becomes more brittle due to heat treatment and its strength characteristics are decreased by 10–30% [2]. It was claimed that degradation of the hemicelluloses between microfibrils in cell wall is the main reason of strength loss in wood [9]. This case especially reveals the importance of determining the strength properties of heat treated woods in terms of structural constructions. Several studies were conducted to determine the effects of heat treatment temperature and duration on compression strength (CS) of wood. Unsal and Ayrilmis [10] found that CS parallel to grain of river red gum (Eucalyptus camaldulensis Dehn.) samples decreased about 19.0% as a result of heat treatment at 180 °C for 10 h. Yıldız et al. [11] investigated the mechanical behavior of spruce wood modified by heat. They observed that the CS losses due to heat treatment were 32.44% at 200 °C for 10 h. Korkut [12] detected a reduction of 29.41% for CS of Uludag fir (Abies bornmuellerinana Mattf.) wood at 180 °C for 10 h. Korkut and Budakçı [13] studied on the mechanical properties of rowan (Sorbus aucuparia L.) wood. They determined a reduction of 24.33% for CS of the samples exposed to the same treatment time and temperature. Similarly, heat treatment causes varying amounts of weight loss (WL) depending on exposure temperature and time. Zaman et al. [14] reported WL of 6.4%, 7.1% and 10.2% for birch (Betula pendula) treated at 205 °C for 4, 6 and 8 h, respectively. It is a fact that a great number of temperature and duration values need to be tested to determine a change in the mechanical behavior of wood caused by heat treatment. However, conducting comprehensive experiments causes the loss of much time and high costs. Therefore, it is very important to find more economic methods providing desirable results concerning CS of heat treated wood without needing the more experiments requiring much time and costs. For this purpose, artificial neural networks (ANNs) have been widely used in the field of wood science, such as calculating wood thermal conductivity [15], moisture analysis in wood [16], predicting fracture toughness of wood [17], wood recognition system [18], forecasting wood quality [19], drying process of wood [20], and wood veneer classification [21]. ANNs have been also used for predicting some mechanical properties of solid wood and wood composites. Cook and Chui [22] predicted the internal bond strength of particleboard using a radial basis function neural network with an accuracy level of 87.5%. Fernández et al. [23] predicted MOR and MOE values of particleboard by ANN at the accuracy levels of 86% and 87%, respectively. Esteban et al. [24] predicted the MOE of Abies pinsapo Boiss. wood by using ANN with 75.0% accuracy. Esteban et al. [25] predicted bonding strength of plywood using an ANN with 93% accuracy. Demirkır et al. [26] predicted the bonding strength of plywood using an ANN at the accuracy level of 98.0%. Studies on predicting some mechanical properties of wood and wood composites were expressed above. However, there is very limited information on predicting CS of heat treated wood. Ulucan [27] predicted CS of heat treated pine and chestnut woods by ANN at the accuracy levels of 92.59% and 92.04%, respectively. Therefore, the main aim of this study was to design the models having capable of predicting CS in heat treated woods by using the values obtained from the experimental study and thus to obtain more economic and safe results without doing comprehensive tests.

In this study, CS values of heat treatment woods were predicted by the ANN and MLR models using the experimental results. According to the obtained data, the following was concluded. A significant decrease was generally observed in CS values from the most important mechanical properties of wood with increasing duration and temperature of heat treatment. Also, WL of samples increased with increasing exposure temperature and time. When the measured values were compared with the predicted values obtained by ANN, it was shown that the ANN modeling technique can be successfully used for predicting the CS values of heat treated woods in a quite short period of time with low error rates. Thus, this study allows a preliminary decision about usability for structural purposes of treated woods. In the testing set, the R2 and MAPE values were obtained as 0.997% and 2.641% respectively. These values obtained by using ANN model showed a very higher prediction performance than MLR model. Considering the cost and time consumed for carrying out the experiment, with the use of ANN model, satisfactory results can be predicted rather than measured which thereby reduces the testing time and cost. Consequently, the losses of time, material and costs can be prevented.