Optimized random forest model for predicting flexural properties of sustainable composites

Abstract

AbstractIn an era of technological advancements, the quest for sustainable products has taken a center stage. The utilization of natural fiber reinforced polymer composites has become crucial in the manufacturing of eco‐friendly products due to their low cost, renewability, biodegradability, and comparable properties to synthetic composites. Coir‐based composites have been utilized in roofing, composite panels, furniture, and bio‐based insulation applications, providing enhanced strength and sustainability. In the current investigation, an exploratory data analysis (EDA) was conducted to understand the relationship between the various input variables and the flexural properties of short coir polymer composites (SCPCs). The data analysis revealed that manufacturing process had a significant impact on the flexural properties of SCPCs. Based on the insights gained from the EDA, an optimized Random Forest prediction model was developed to predict the flexural properties. Genetic algorithm approach for hyperparameter optimization led to lower objective loss in contrast to Bayesian optimized Random Forest model. The model's performance was subsequently evaluated through holdout validation, and the outcome demonstrated the model's proficiency in accurately predicting the properties. The developed model can be used as a tool for optimizing the design of SCPCs for specific applications, by predicting the flexural properties of the composites.Highlights Flexural behavior of short coir polymer composites was analyzed and modeled. Manufacturing process has the highest impact on the flexural properties. Random forest model predicted the flexural properties with high accuracy. Genetic algorithm optimized model further enhanced the model performance. The developed framework provides insights into designing biocomposites.