Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers

Faris M. AL-Oqla; Hossam Faris; Maria Habib; Pedro A. Castillo

doi:10.28991/ESJ-2023-07-06-02

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Faris M. AL-Oqla
Fmaloqla@hu.edu.jo
Department of Mechanical Engineering, Faculty of Engineering, The Hashemite University, P.O box, 330127, Zarqa 13133,, Jordan
Hossam Faris 2) King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan. 3) Research Centre for Information and Communications Technologies of the University of Granada, (CITIC-UGR), University of Granada, 18011 Granada,, Spain
Maria Habib Doctoral School of Sciences, Technologies, and Engineering, the University of Granada, 18011, Granada,, Spain
Pedro A. Castillo Department of Computer Engineering, Automatics and Robotics, ETSIIT-CITIC, University of Granada, 18011 Granada,, Spain

Vol. 7 No. 6 (2023): December

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Advanced modern technology and the industrial sustainability theme have contributed to the implementation of composite materials for various industrial applications. Bio-composites are among the desired alternatives for green products. However, to properly control the performance of bio-composites, predicting their constituent properties is of paramount importance. This work introduces an innovative, evolving genetic programming tree model for predicting the mechanical properties of natural fibers for the first time 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 the training/testing phases. Robust models were developed utilizing evolving genetic programming tree models to predict the tensile strength, Young's modulus, and the elongation at break properties of the natural fibers. It was revealed that the 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 would facilitate utilizing artificial intelligence to predict the overall mechanical properties of natural fibers without exhausting experimental efforts and cost to enhance the development of better green composite materials for various industrial applications.

Doi: 10.28991/ESJ-2023-07-06-02

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AL-Oqla, F. M., Faris, H., Habib, M., & Castillo, P. A. (2023). Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers. Emerging Science Journal, 7(6), 1863–1874. https://doi.org/10.28991/ESJ-2023-07-06-02

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Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers

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Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers

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Most Cited Articles

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic

Impediments of Green Finance Adoption System: Linking Economy and Environment