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.
Highlights
Wood modification is a cutting-edge research direction in forestry development, andHill [1] stated that wood modification usually uses chemical, biological, or physical reagents and materials to improve the properties that people demand from wood within a certain service life
TSSA-Back Propagation (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; this article provided a feasible prediction algorithm for the field related to the mechanical property changes of wood after heat treatment
The sparrow search algorithm is a heuristic algorithm proposed by Xue Jiankai et al [19] in 2020, which simulated the behavior of sparrows in nature
Summary
Wood modification is a cutting-edge research direction in forestry development, and. Hill [1] stated that wood modification usually uses chemical, biological, or physical reagents and materials to improve the properties that people demand from wood within a certain service life. SH Lee et al [11] believed that, after oil heat treatment, the equilibrium moisture content of wood decreases, the compressive strength along the grain increases, and the change of mechanical properties brings strong moisture resistance, dimensional stability, and biological durability, which can be applied to floors and outdoor buildings. Nasir V. et al [14] developed a group method of the data handling (GMDII) neural network model with color parameters of western hemlock as input to predict the physical properties such as equilibrium moisture content, density, water absorption, coefficient of expansion, and modulus of elasticity of wood. In terms of data prediction, many papers in the wood property prediction literature used a more single model, less used other algorithmic model comparison, which makes it difficult to discern which is more suitable for the prediction of wood performance, so this paper optimized the algorithmic model twice and compared with the original model, can highlight the improvements of the optimization method and the practicality of the optimized model
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