Prediction of Mechanical Properties of Thermally Modified Wood Based on TSSA-BP Model

Abstract

In order to demonstrate whether the sparrow search algorithm can show good performance in optimization, this paper improves the prediction model by this algorithm and predicts the change data of wood mechanical properties under different conditions, which better reflects the connection between the process parameters of wood heat treatment and the change of wood mechanical properties. The article takes the five main mechanical property parameters of thermally modified wood: compressive strength along the grain, flexural strength, flexural elastic modulus, radial hardness, and tangential hardness, respectively, as the objects of study and improves the sparrow search algorithm by Tenting chaotic mapping and then optimizes the Back Propagation (BP) network model by this algorithm. The results show that the number of iterations of the optimized Tent-Sparrow search algorithm-Back Propagation network model (TSSA-BP) is only one-eighth that of the original BP network model, and the convergence speed is greatly improved, the root mean square error of the TSSA-BP model is at least one-half times that of the original BP model, and the optimized model fits the original data better in terms of predicted values; thus, this article provided a feasible prediction algorithm for the field related to the mechanical property changes of wood after heat treatment.

Mechanical Properties of Wood Prediction Based on the NAGGWO-BP Neural Network

“…Smaller values indicate better model prediction performance. The experimental results are shown in Table 2, and the TSSA-BP data were obtained from the literature [38]. BP denotes the original backpropagation neural network.…”

“…BP denotes the original backpropagation neural network. NAGGWO-BP, GWO-BP, and TSSA-BP denote the BP neural network after its optimization using the NAGGWO, GWO [23], and TSSA [38] models. As shown in Table 1, the MAE, MSE, and MAPE values of the NAGGWO-BP neural network prediction model are much smaller than the prediction errors of other models.…”

“…We used the same training and testing specimens as those found in the literature [38] to ensure the fairness of the model comparison. The first 58 sets of experimental data in Appendix A Table A1 were used as the training set, and the last 30 sets of experimental data were classified as the testing set.…”

Effects of UV radiation on natural and synthetic materials

“…Thermal treatment yields appealingly darker [93,94] treated wood that is less hygroscopic and has improved dimensional stability [95]. Neural network-based algorithms that predict the properties of treated wood as a function of variables such as temperature, are being developed [96]. However, these improvements are obtained at the expense of its UV resistance with the treated wood discolouring readily on exposure to solar UV radiation [97,98].…”

Drying kinetics prediction and quality effect of ultrasonic synergy vacuum far‐infrared drying of Codonopsis pilosula

“…A supervised back‐propagation (BP) neural network has the benefits of nonlinearity, fault tolerance, and self‐adaptation and might be used as an alternative method to predict drying characteristics. The BP neural network methodology has been applied to the simulation of potato drying kinetics (Wang et al., 2020), the prediction and optimization of turmeric volatile oil yield (Akbar et al., 2018), and the prediction of wood mechanical properties (Li & Wang, 2022). However, as a result of the initial value has a great influence on the training effect, it is easy to fall into local optimum.…”