Machine Learning Approach for Mechanical Property Assessment of Industrial Waste-Filled Epoxy-Jute Composites

This study investigates the development, characterization, and predictive modelling of sustainable epoxy composites reinforced with jute fiber and Linz-Donawitz (LD) sludge of industrial waste. Specimens were made using different LD sludge content (0–25 wt.%) and the constant jute fiber loading of 20 wt.%. The mechanical testing showed that the best composition was 60 wt.% epoxy, 20 wt.% jute, 20 wt.% LD sludge that demonstrated considerable gains in tensile strength (up to 61.84 MPa, an improvement of 28.8%), flexural strength (up to 31.81 MPa, an improvement of 41.8%), and impact strength (up to 18.026 kJ/m ² ). After 20 wt.% sludge, the mechanical performance decreased because of interfacial defects. Machine learning models, namely Decision Tree, Random Forest, Gradient Boosting, and XGBoost were implemented in Google Colab to predict mechanical properties from compositional inputs. XGBoost showed better predictive performance as its error measures were close to zero (MAE = 0.0005 MPa tensile strength), validating its utility for inverse material design and the optimization of sustainable hybrid composites. The findings highlight a combined waste-valorisation and data-driven design approach that supports the development of sustainable composites for structural and industrial use.