Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Neural and Evolutionary Computing

arXiv:2404.07213 (cs)
[Submitted on 20 Feb 2024]

Title:Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers for Better Biocomposite Materials

Authors:Faris M. AL-Oqla, Hossam Faris, Maria Habib, Pedro A. Castillo-Valdivieso
View PDF
Abstract:Advanced modern technology and industrial sustainability theme have contributed implementing composite materials for various industrial applications. Green composites are among the desired alternatives for the green products. However, to properly control the performance of the green composites, predicting their constituents properties are of paramount importance. This work presents an innovative evolving genetic programming tree models for predicting the mechanical properties of natural fibers based upon several inherent chemical and physical properties. Cellulose, hemicellulose, lignin and moisture contents as well as the Microfibrillar angle of various natural fibers were considered to establish the prediction models. A one-hold-out methodology was applied for training/testing phases. Robust models were developed to predict the tensile strength, Young's modulus, and the elongation at break properties of the natural fibers. It was revealed that Microfibrillar angle was dominant and capable of determining the ultimate tensile strength of the natural fibers by 44.7% comparable to other considered properties, while the impact of cellulose content in the model was only 35.6%. This in order would facilitate utilizing artificial intelligence in predicting the overall mechanical properties of natural fibers without experimental efforts and cost to enhance developing better green composite materials for various industrial applications.
Subjects: Neural and Evolutionary Computing (cs.NE); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
Cite as: arXiv:2404.07213 [cs.NE]
  (or arXiv:2404.07213v1 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.2404.07213

Submission history

From: Pedro A. Castillo [view email]
[v1] Tue, 20 Feb 2024 16:55:57 UTC (883 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
license icon view license
Current browse context:
cs.NE
< prev   |   next >
new | recent | 2024-04
Change to browse by:
cond-mat
cond-mat.mtrl-sci
cs
cs.AI

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)