Bayesian-based durability life prediction method of nano-modified concrete

Due to the low prediction accuracy of traditional concrete durability life prediction methods, a Bayesian-based durability life prediction method for nano-modified concrete is proposed. Firstly, the diffusion function of CO2 in concrete is established through Fick's first law, and the carbonation depth of concrete is determined based on regression analysis. Then, according to the damage mechanics theory, the freeze-thaw cycle damage function of nano-modified concrete is established. Finally, according to the Bayesian theorem and the binary normal distribution relationship, combined with the prediction criteria, a Bayesian-based durability life prediction model of nano-modified concrete is established, and the determined concrete carbonation depth is input into the prediction model to output the prediction results. The experimental results show that the prediction accuracy of concrete durability life is high.

Automated summary

This study proposes a Bayesian-based durability life prediction method for nano-modified concrete to address the low prediction accuracy of traditional methods. The diffusion function of CO2 in concrete is established using Fick's first law, and carbonation depth is determined through regression analysis. Additionally, a freeze-thaw cycle damage function for nano-modified concrete is established based on damage mechanics theory. Experimental results demonstrate high prediction accuracy for concrete durability life.