Version 1
: Received: 14 December 2023 / Approved: 14 December 2023 / Online: 14 December 2023 (14:01:38 CET)
How to cite:
Huang, J.; Kuang, Z.; Ma, J.; Fang, Y. Causal Analysis of Wood Performance Based on Liang-Kleeman Information Flow. Preprints2023, 2023121106. https://doi.org/10.20944/preprints202312.1106.v1
Huang, J.; Kuang, Z.; Ma, J.; Fang, Y. Causal Analysis of Wood Performance Based on Liang-Kleeman Information Flow. Preprints 2023, 2023121106. https://doi.org/10.20944/preprints202312.1106.v1
Huang, J.; Kuang, Z.; Ma, J.; Fang, Y. Causal Analysis of Wood Performance Based on Liang-Kleeman Information Flow. Preprints2023, 2023121106. https://doi.org/10.20944/preprints202312.1106.v1
APA Style
Huang, J., Kuang, Z., Ma, J., & Fang, Y. (2023). Causal Analysis of Wood Performance Based on Liang-Kleeman Information Flow. Preprints. https://doi.org/10.20944/preprints202312.1106.v1
Chicago/Turabian Style
Huang, J., Jingchao Ma and Yixuan Fang. 2023 "Causal Analysis of Wood Performance Based on Liang-Kleeman Information Flow" Preprints. https://doi.org/10.20944/preprints202312.1106.v1
Abstract
To accurately identify high-quality functional wood with superior mechanical properties and enhance its durability, it is imperative to understand the relationships between the physical and mechanical attributes of diverse wood types. Presently, there exist issues related to accuracy, efficiency, and differentiation in studying these performance correlations. The Liang Kleeman (L-K) information flow theory, a causative analytical approach rooted in fundamental physics principles, offers swift computational capabilities. Furthermore, the coefficient of variation in L-K information flow quantitatively gauges the intensity of causal effects. Therefore, this article adopts a causal artificial intelligence perspective and introduces a wood performance analysis method based on L-K information flow.This study focuses on four Chinese fir varieties, including fast-growing Chinese fir (YKS), red heart Chinese fir (CSH, XXH), and iron heart Chinese fir (XXT). By computing and comparing variation coefficients of L-K information flow for various physical properties and their impact on mechanical properties, causal relationships between these properties are elucidated. These findings are then applied to predict wood mechanical properties, achieving a remarkable maximum accuracy of 90%.In the future, these findings will prove invaluable for the selection of high-quality wood and for improving the manufacturing process to prolong the service life of functionalized wood.
Keywords
wood; mechanical properties; causal influence; Liang-Kleeman information flow; performance prediction
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
