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This study aims to investigate the behavior of the green biomass-derived copper (lignin/silica/fatty acids) complex, copper lignin/silica/fatty acids (Cu-LSF) complex, when incorporated into the nonpolar ethylene propylene diene (EPDFM) rubber matrix, focusing on its reinforcing and antioxidant effect on the resulting EPDM composites.

The structure of the prepared EPDM composites was confirmed by Fourier-transform infrared spectroscopy, and the dispersion of the additive fillers and antioxidants in the EPDM matrix was investigated using scanning electron microscopy. Also, the rheometric characteristics, mechanical properties, swelling behavior and thermal gravimetric analysis of all the prepared EPDM composites were explored as well.

Results revealed that the Cu-LSF complex dispersed well in the nonpolar EPDM rubber matrix, in thepresence of coupling system, with enhanced Cu-LSF-rubber interactions and increased cross-linking density, which reflected on the improved rheological and mechanical properties of the resulting EPDM composites. From the various investigations performed in the current study, the authors can suggest 7–11 phr is the optimal effective concentration of Cu-LSF complex loading. Interestingly, EPDM composites containing Cu-LSF complex showed better antiaging performance, thermal stability and fluid resistance, when compared with those containing the commercial antioxidants (2,2,4-trimethyl-1,2-dihydroquinoline and N-isopropyl-N’-phenyl-p-phenylenediamine). These findings are in good agreement with our previous study on polar nitrile butadiene rubber.

The current study suggests the green biomass-derived Cu-LSF complex to be a promising low-cost and environmentally safe alternative filler and antioxidant to the hazardous commercial ones.

The application of steel fiber reinforced concrete (SFRC) beams is limited in practice, partially due to the lack of accurate shear strength prediction models. This study aims to develop a reliable shear strength prediction model for SFRC beams.

In this study, an artificial neural network was employed to predict the shear strength of SFRC beams, utilizing a comprehensive database of 562 experimental studies. Multiple neural networks were established with varying hyperparameters, and their performance was evaluated using statistical parameters.

The neural network with 11 neurons showed superior results than other networks. The performance evaluation, efficiency and accuracy of the selected neural network were examined using margin of deviation, k-fold cross-validation, Shapley analysis, sensitivity analysis and parametric analysis. The proposed artificial neural network model accurately predicts the shear strength and outperforms other existing equations.

This research contributes to overcoming the limitations of existing prediction models for shear strength of SFRC beams without stirrups by developing a highly accurate model based on ANN. Utilizing a comprehensive database and rigorous evaluation techniques enhances the reliability and applicability of the proposed model in practical engineering applications.