Mechanical Property Prediction of Wood Using a Backpropagation Neural Network Optimized by Adaptive Fractional-Order Particle Swarm Algorithm

This study proposes a novel LK-BP-AFPSO model for the nondestructive evaluation of wood mechanical properties, combining a backpropagation neural network (BP) with adaptive fractional-order particle swarm optimization (AFPSO) and Liang–Kleeman (LK) information flow theory. The model accurately predi...

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Bibliographic Details
Published in:Forests Vol. 16; no. 8; p. 1223
Main Authors: Huang, Jiahui, Kuang, Zhufang
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2025
Subjects:
Accuracy
Algorithms
Analysis
Artificial intelligence
Back propagation
Back propagation networks
Bend strength
Compressive strength
Efficiency
Feature selection
Flow theory
Information flow
Information management
Machine learning
Mathematical optimization
Mechanical properties
Methods
Modulus of elasticity
Neural networks
Nondestructive testing
Optimization algorithms
Particle swarm optimization
Performance evaluation
Quality assessment
Quality control
Shear strength
Tensile strength
Wood
China
ISSN:1999-4907, 1999-4907
Online Access:Get full text
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Summary:This study proposes a novel LK-BP-AFPSO model for the nondestructive evaluation of wood mechanical properties, combining a backpropagation neural network (BP) with adaptive fractional-order particle swarm optimization (AFPSO) and Liang–Kleeman (LK) information flow theory. The model accurately predicts four key mechanical properties—longitudinal tensile strength (SPG), modulus of elasticity (MOE), bending strength (MOR), and longitudinal compressive strength (CSP)—using only nondestructive physical features. Tested across diverse wood types (fast-growing YKS, red-heart CSH/XXH, and iron-heart XXT), the framework demonstrates strong generalizability, achieving an average prediction accuracy (R2) of 0.986 and reducing mean absolute error (MAE) by 23.7% compared to conventional methods. A critical innovation is the integration of LK causal analysis, which quantifies feature–target relationships via information flow metrics, effectively eliminating 29.5% of spurious correlations inherent in traditional feature selection (e.g., PCA). Experimental results confirm the model’s robustness, particularly for heartwood variants, while its adaptive fractional-order optimization accelerates convergence by 2.1× relative to standard PSO. This work provides a reliable, interpretable tool for wood quality assessment, with direct implications for grading systems and processing optimization in the forestry industry.
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ISSN:1999-4907
1999-4907
DOI:10.3390/f16081223