  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.
