所属单位：

土木工程学院

发表刊物：

4th International Conference on the Durability of Concrete Structures

关键字：

中文关键字：carbonation depth, concrete, durability, neural ne，英文关键字：

摘要：

Concrete carbonation is one of the most significant causes of deterioration of reinforced concrete structures in atmospheric environment. However, current models based on the laboratory tests cannot predict carbonation depth accurately. In this paper, the BP neural network is optimized by the particle swarm optimization (PSO) algorithm to establish the model of the length of the partial carbonation zone for concrete. After simulation training, the improved model is applied to a concrete bridge for carbonation depth prediction. The results show that the improved model, which has faster a convergence rate, has a good ability in predicting the length of the partial carbonation zone of the reinforced concrete, and the predicted value matches the field-measured value very well, which provides scientific guidance to durability design, assessment, and life prediction for concrete structures.

备注：

牛荻涛

合写作者：

DitaoNiu,ZhenpingDong

第一作者：

牛荻涛

论文类型：

期刊论文

卷号：

卷：

期号：

期：

页面范围：

页：66-71

是否译文：

否

发表时间：

2014-07-01