Mechanical characteristics of multi-scale carbon nanotube-carbon fiber reinforced concrete and a dynamic constitutive model within an EP damage framework

Multi-scale Carbon Nanotube-carbon Fiber (MCNF) are widely employed in concrete reinforcement due to their exceptional interfacial properties. However, current research on such fiber-reinforced concrete primarily focuses on static mechanical performance and has yet to establish a comprehensive theoretical framework. Moreover, traditional damage models exhibit certain limitations in characterizing the damage evolution process of concrete. Hence, this study takes Multi-scale Carbon Nanotube-carbon Fiberconcrete (MCNF concrete) as the research subject, establishing a predictive model for the dynamic compressive strength of MCNF concrete based on porosity theory. By introducing damage and strength growth factors, a dynamic mechanical constitutive model for multi-scale fiber-reinforced concrete is formulated. Additionally, the Mazars damage model is refined according to practical conditions, and an improved LM damage model is proposed by integrating the advantages of both the Loland and Mazars models. Furthermore, based on the effective bearing area theory and porosity theory, a novel concrete damage model (EP model) is developed and compared with the Mazars and LM damage models. Research findings indicate that, compared to the Mazars model, the LM model overcomes its limitations due to linear phase assumptions and better aligns with actual stress conditions. In terms of goodness-of-fit, the LM model exhibits an increase in the average correlation index R² from 0.975 to 0.980 and a reduction in the average root mean square error (RMSE) from 3.55 to 3.22, demonstrating enhanced predictive accuracy. However, the LM model is prone to abrupt changes at specific points. In contrast, the newly established EP model maintains comparable R² and RMSE values to the LM model while achieving better consistency with the concrete stress-strain curve without abrupt changes, indicating superior engineering applicability.