Abstract
The probabilistic characterization of tensile strength of potassium hydroxide (KOH) treated jute fiber is conducted in the light of machine learning methods. The concentration of KOH, soaking periodand drying temperature are considered as the input features. The experimental results after surface treatment with KOHare utilized to determine the machine learning models. The predictive capabilities of linear regression (LR) and Gaussian process regression (GPR) based modelsare compared and it has been found that the latter is more accurate than the former. Further, the data-driven sensitivity analysis and uncertainty quantification arecarried out with the help of GPRmodel. The overall probabilistic results reported in this articlewould provide the complete insight regarding tensile strength of potassium hydroxide (KOH) treated jute under the uncertain variations in the process parameters.Thestudy also provides the total design domain of treated jute under the practically relevant uncertainty associated with the discussed process parameters.
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