Prediction of Concrete Structure Service Life Based on the Principle of Neural Connections in Brain Circuits

Abstract

Concrete is the most widely used structural material in the world; however, its structural safety is affected by concrete carbonization and reinforcement corrosion. According to the principles and characteristics of concrete carbonization and reinforcement corrosion, this paper establishes artificial neural network prediction and evaluation models for the depth of carbonization and the degree of reinforcement corrosion based on the principle of neural connections in brain circuits. The results show that the artificial neural network prediction models are of high accuracy, and that the RBF neural network prediction model is more accurate and requires far less training time than the BP neural network model.