Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers for Better Biocomposite Materials
<|reference_start|>Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers for Better Biocomposite Materials: 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.<|reference_end|>
@article{al-oqla2024evolving,
    title={Evolving Genetic Programming Tree Models for Predicting the Mechanical
  Properties of Green Fibers for Better Biocomposite Materials},
    author={Faris M. AL-Oqla, Hossam Faris, Maria Habib, Pedro A.
  Castillo-Valdivieso},
    journal={arXiv preprint arXiv:2404.07213},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07213},
    primaryClass={cs.NE cond-mat.mtrl-sci cs.AI}
}